Neural typographical error modeling via generative adversarial networks

ABSTRACT

Systems and processes for operating an intelligent automated assistant are provided. In one example process, one or more input words can be received. The process can extract, based on the one or more input words, seed data for unsupervised training of a first learning network. Training data that includes a collection of words having typographical errors for the first learning network can be obtained. The process can determine, using the first learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data. The one or more output words can include typographical errors. The process can generate, based on the determined one or more output words, a data set for supervised training of a second learning network. The second learning network can provide one or more typographical error suggestions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No. 62/738,651, filed Sep. 28, 2018, entitled “Neural Typographical Error Modeling via Generative Adversarial Networks,” and U.S. Provisional Application No. 62/779,980, filed Dec. 14, 2018, entitled “Neural Typographical Error Modeling via Generative Adversarial Networks.” The contents of both applications are hereby incorporated by reference in their entirety for all purposes.

FIELD

This relates generally to intelligent automated assistants and, more specifically, to intelligent detection and correction of typographical errors within user input text.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide a beneficial interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can provide a speech input including a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from the speech input and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more services of the electronic device, and a relevant output responsive to the user request can be returned to the user.

In some circumstances, intelligent automated assistants can perform tasks automatically without receiving a request from a user. For example, a user may enter text into an application in order to perform a task, such as sending a text message or email to a friend. While entering the text, the user may enter a word that is spelled incorrectly. In such cases, the intelligent automated assistant may recognize that the word is spelled incorrectly and provide the user with a notification that the word is spelled incorrectly. However, detection of words that are spelled incorrectly may be limited by merely following a dictionary and thus may ignore certain errors that the user makes. As an example, an intelligent automated assistant may not recognize that a word has a typographical error if the word is spelled correctly but contextually incorrect. As another example, the detection of words that are spelled incorrectly may be limited to errors that the user has previously made and corrected a predetermined number of times.

SUMMARY

Systems and processes for providing intelligent correction of typographical errors are provided.

Example methods are disclosed herein. An example method includes, at one or more electronic devices having one or more processors and memory, receiving one or more input words. The method also includes extracting, based on the one or more input words, seed data for unsupervised training of a first learning network. The method further includes obtaining training data for the first learning network. The training data includes a collection of words having typographical errors. The method further includes determining, using the first learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data. The one or more output words include typographical errors. The method also includes generating, based on the determined one or more output words, a data set for supervised training of a second learning network. The trained second learning network provides one or more typographical error correction suggestions.

An example method includes, at an electronic device having one or more processors and memory, receiving a user input including one or more words. The method also includes displaying the user input. The method further includes determining, using a trained first learning network, whether the user input includes a typographical error. The first learning network is trained in a supervised manner based on a data set generated by a second learning network, and the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors. The method further includes, in accordance with a determination that the user input includes a typographical error, displaying one or more correction suggestions or correcting the displayed user input.

Example non-transitory computer-readable media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions which, when executed by one or more processors of an electronic device, cause the electronic device to receive one or more input words. The one or more programs also include instructions that cause the electronic device to extract, based on the one or more input words, seed data for unsupervised training of a first learning network. The one or more programs further include instructions that cause the electronic device to obtain training data for the first learning network. The training data includes a collection of words having typographical errors. The one or more programs further include instructions that cause the electronic device to determine, using the first learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data. The one or more output words include typographical errors. The one or more programs further include instructions that cause the electronic device to generate, based on the determined one or more output words, a data set for supervised training of a second learning network. The trained second learning network provides one or more typographical error correction suggestions.

An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions, which when executed by one or more processors of an electronic device, cause the electronic device to receive a user input including one or more words. The one or more programs also include instructions that cause the electronic device to display the user input. The one or more programs further include instructions that cause the electronic device to determine, using a trained first learning network, whether the user input includes a typographical error. The first learning network is trained in a supervised manner based on a data set generated by a second learning network and the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors. The one or more programs further include instructions that cause the electronic device to in accordance with a determination that the user input includes a typographical error, display one or more correction suggestions or correct the displayed user input.

Example electronic devices are disclosed herein. An example electronic device comprises one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for receiving one or more input words. The one or more programs also include instructions for extracting, based on the one or more input words, seed data for unsupervised training of a first learning network. The one or more programs further include instructions for obtaining training data for the first learning network. The training data includes a collection of words having typographical errors. The one or more programs further include instructions for determining, using the first learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data. The one or more output words include typographical errors. The one or more programs further include instructions for generating, based on the determined one or more output words, a data set for supervised training of a second learning network. The trained second learning network provides one or more typographical error correction suggestions.

An example electronic device comprises one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for receiving a user input including one or more words. The one or more programs also include instructions for displaying the user input. The one or more programs further include instructions for determining, using a trained first learning network, whether the user input includes a typographical error. The first learning network is trained in a supervised manner based on a data set generated by a second learning network and the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors. The one or more programs also include instructions for, in accordance with a determination that the user input includes a typographical error, displaying one or more correction suggestions or correcting the displayed user input.

An example electronic device comprises means for receiving one or more input words. The electronic device also includes means for extracting, based on the one or more input words, seed data for unsupervised training of a first learning network. The electronic device further includes means for obtaining training data for the first learning network. The training data includes a collection of words having typographical errors. The electronic device further includes means for determining, using the first learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data. The one or more output words include typographical errors. The electronic device further includes means for generating, based on the determined one or more output words, a data set for supervised training of a second learning network. The trained second learning network provides one or more typographical error correction suggestions.

An example electronic device comprises means for receiving a user input including one or more words. The electronic device also includes means for displaying the user input. The electronic device further includes means for determining, using a trained first learning network, whether the user input includes a typographical error. The first learning network is trained in a supervised manner based on a data set generated by a second learning network and the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors. The electronic device further includes means for, in accordance with a determination that the user input includes a typographical error, displaying one or more correction suggestions or correcting the displayed user input.

Techniques for intelligent detection and correction of typographical errors are desirable. In particular, it is desirable to detect both non-atomic typographical errors and atomic typographical errors. A word having non-atomic typographical error is lexically incorrect (e.g., “moove” instead of “move” or “bakler” instead of “baker”). A word having atomic typographical error is lexically correct but contextually incorrect. For example, the word “prostate” is used in the sentence “the prostate figure on the ground” when the word “prostrate” is actually intended; or the word “nuclear” is used in a sentence “your answer is nuclear” when the word “unclear” is actually intended.

Existing techniques for typographical error correction often require training a learning network in a supervised manner to generate a typographical error model directly from user-produced erroneous entries. A typographical error model is sometimes also referred to as a language model for correcting typographical errors. This type of training is often limited, difficult, and impractical. For example, it is impractical to generate a typographical error model representing all conceivable typographical errors that users may make, atomic and non-atomic. As a result, conventional typographical error models typically cover a limited set of frequently-made non-atomic typographical errors, and may not cover atomic typographical errors at all. Further, because conventional typographical error models are generated based on a limited set of frequently-made errors, a device that operates using the conventional typographical error models is incapable of recognizing many other typographical errors (e.g., a typographic error that has not been encountered in the past). As a result, the user experience may be negatively affected (e.g., become frustrated or annoyed) and human-machine user interface efficiency may be reduced. Moreover, a device that operates using conventional typographical error models may detect and correct the exact error made by the user for a particular word. But it may not be able to detect and correct errors of a same or similar kind associated with the same word or different words.

Techniques for providing more intelligent typographical error detection and correction are thus desirable. In some embodiments, a language model for more intelligent detection and correction of typographical errors can be generated by a combination of unsupervised training and supervised training of learning networks. In some embodiments, the unsupervised training of a learning network can generate a data set based on input words and training data including words with typographical errors. The data set can be used in a supervised training of another learning network to generate the language model. As described in more detail below, to provide more intelligent detection and correction of typographical errors, performing an unsupervised training of a learning network reduces or eliminates the need for an impractically-large data set including all conceivable typographical errors required for performing a supervised training. In particular, a first learning network can be trained in an unsupervised manner to generate a data set that includes realistic typographical errors, rather than all conceivable errors.

Realistic typographical errors can include errors that are actually made by human users in the past and errors that resemble those made by human users (e.g., errors that are generated by a computing device and have the same or similar probability distribution as errors actually made by human users). For example, a human user may likely make a typographical error such as “teh” for the word “the.” But a human user may not or seldom make a typographical error such as “eth” for the word “the.” An unsupervised training of a learning network described herein can generate a data set including similar errors for other words (e.g., “seh” for the word “she”) even if a user has not made the exact errors before. The typographical errors of a data set thus generated by the unsupervised training has probability distributions that are the same or similar to those made by human users. The data set therefore includes realistic typographical errors and can then be used for training another learning network in a supervised manner. As a result, generating a training data set that includes all conceivable typographical errors is not required to provide more intelligent typographical error detection and correction. In addition, the data set generated by the unsupervised training can include both atomic and non-atomic typographical errors. In this way, training data that includes realistic typographical errors can be generated in an efficient and practical manner for generating an improved typographical error model of a digital assistant. Thus, the improved typographical error model represents more realistic typographical errors (e.g., including errors that are not made by human users in the past but resemble those made by human users) and is an improvement over conventional typographical error models (e.g., models that only represent errors made by human users in the past). Further, the improved typographical error model may provide more accurate detection of typographical errors and suggest more intelligent corrections to the user, resulting in improved user experience and more efficient human-machine interfaces.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment for implementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples.

FIG. 2B is a block diagram illustrating exemplary components for event handling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing the client-side portion of a digital assistant, according to various examples.

FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu of applications on a portable multifunction device, according to various examples.

FIG. 5B illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display, according to various examples.

FIG. 6A illustrates a personal electronic device, according to various examples.

FIG. 6B is a block diagram illustrating a personal electronic device, according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or a server portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG. 7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to various examples.

FIG. 8 illustrates a block diagram of a digital assistant for generating a typographical error model, according to various examples.

FIG. 9 illustrates a block diagram of a seed data extractor for extracting seed data for unsupervised training of a learning network, according to various examples.

FIG. 10 illustrates a block diagram of a learning network for generating a data set of words including typographical errors through unsupervised training, according to various examples.

FIG. 11 illustrates a user interface for receiving user input text, according to various examples.

FIG. 12 illustrates a block diagram of a digital assistant for providing a corrected word, according to various examples.

FIGS. 13A-13B illustrate user interfaces for providing candidate words absent typographical errors to the user, according to various examples.

FIGS. 14A-14D illustrate an exemplary process for operating a digital assistant to provide typographical error detection and correction, according to various examples.

FIG. 15 illustrates a process for operating a digital assistant to provide typographical error detection and correction, according to various examples

DETAILED DESCRIPTION

In the following description of examples, reference is made to the accompanying drawings in which are shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples.

In some examples, input words are used to generate seed data for unsupervised training of a learning network. Based on the seed data and training data that includes a collection of words having typographical errors (e.g., a limited collection of words having errors made by human users), the learning network may be trained in an unsupervised manner to generate a data set (e.g., a plurality of words including realistic typographical errors) for supervised training of another learning network. Training the learning network in an unsupervised manner allows for the generation of a data set that includes realistic typographical errors. For example, the data set generated by the learning network after an unsupervised training can include a plurality of words that have typographical errors similar to those made by human users (e.g., they have similar probability distributions).

The data set thus generated by unsupervised training of the learning network (described in more detail below) can enable more intelligent and accurate typographical error detection. In particular, the data set can be used to train another learning network in a supervised manner to generate an improved typographical error model. The improved typographical error model can be used in a digital assistant operating on a user device to more intelligently detect and correct typographical errors, whether the errors were made in past by human users or not. Moreover, the improved typographical error model can be used for correcting user input text across different applications. For example, the improved typographical error model can be referenced by or integrated to different applications such as messaging applications, dictation applications, text editing applications, speech recognition applications, searching applications, or the like, to correct typographical errors. As a result, the improved typographical error model is application independent and can be implemented via a digital assistant to interact with any number of applications operating on a user device. Accordingly, the improved typographical error model provides the user with enhanced typographical error detection and correction regardless of the type of application with which a user interacts.

In some examples, the learning network trained in an unsupervised manner can include a generative adversarial network (GAN). A GAN is often used for image processing applications, but to date has been less widely used in natural language processing. In particular, in performing image processing tasks, perturbing one or more image pixels in a small amount during the unsupervised training using, for example, a GAN is unlikely to render significant changes to the image. In contrast, in performing natural language processing tasks, even a slight changing of a position of a character or a word may completely change the meaning of the word or sentence. However, as described in more detail below, unsupervised training using, for example, a GAN can be helpful in generating a data set including realistic typographical errors for providing an enhanced typographical error model.

Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first learning network could be termed a second learning network, and, similarly, a second learning network could be termed a first learning network, without departing from the scope of the various described examples. The first learning network and the second learning network are both learning networks and, in some cases, are separate and different learning networks.

The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to various examples. In some examples, system 100 implements a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system performs one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.

Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request includes a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user asks the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant answers, “You are in Central Park near the west gate.” The user also requests the performance of a task, for example, “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user's friends listed in the user's electronic address book. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implemented according to a client-server model. The digital assistant includes client-side portion 102 (hereafter “DA client 102”) executed on user device 104 and server-side portion 106 (hereafter “DA server 106”) executed on server system 108. DA client 102 communicates with DA server 106 through one or more networks 110. DA client 102 provides client-side functionalities such as user-facing input and output processing and communication with DA server 106. DA server 106 provides server-side functionalities for any number of DA clients 102 each residing on a respective user device 104.

In some examples, DA server 106 includes client-facing I/O interface 112, one or more processing modules 114, data and models 116, and I/O interface to external services 118. The client-facing I/O interface 112 facilitates the client-facing input and output processing for DA server 106. One or more processing modules 114 utilize data and models 116 to process speech input and determine the user's intent based on natural language input. Further, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, DA server 106 communicates with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multifunctional device (e.g., device 200, described below with reference to FIG. 2A), a multifunctional device (e.g., device 400, described below with reference to FIG. 4), or a personal electronic device (e.g., device 600, described below with reference to FIG. 6A-6B.) A portable multifunctional device is, for example, a mobile telephone that also includes other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices include the Apple Watch®, iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices include, without limitation, earphones/headphones, speakers, and laptop or tablet computers. Further, in some examples, user device 104 is a non-portable multifunctional device. In particular, user device 104 is a desktop computer, a game console, a speaker, a television, or a television set-top box. In some examples, user device 104 includes a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device 104 optionally includes one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail.

Examples of communication network(s) 110 include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 is implemented using any known network protocol, including various wired or wireless protocols, such as, for example, Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.

Server system 108 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 also employs various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 via second user device 122. Second user device 122 is similar or identical to user device 104. For example, second user device 122 is similar to devices 200, 400, 600, 1100, or 1300 described below with reference to FIGS. 2A, 4, 6A-6B, 11, and 13A-13B. User device 104 is configured to communicatively couple to second user device 122 via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device 122 is configured to act as a proxy between user device 104 and DA server 106. For example, DA client 102 of user device 104 is configured to transmit information (e.g., a user request received at user device 104) to DA server 106 via second user device 122. DA server 106 processes the information and returns relevant data (e.g., data content responsive to the user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicate abbreviated requests for data to second user device 122 to reduce the amount of information transmitted from user device 104. Second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server 106. This system architecture can advantageously allow user device 104 having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server 106 by using second user device 122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server 106. While only two user devices 104 and 122 are shown in FIG. 1, it should be appreciated that system 100, in some examples, includes any number and type of user devices configured in this proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), in some examples, the functions of a digital assistant are implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant can vary in different implementations. For instance, in some examples, the DA client is a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant. FIG. 2A is a block diagram illustrating portable multifunction device 200 with touch-sensitive display system 212 in accordance with some embodiments. Touch-sensitive display 212 is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device 200 includes memory 202 (which optionally includes one or more computer-readable storage mediums), memory controller 222, one or more processing units (CPUs) 220, peripherals interface 218, RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, input/output (I/O) subsystem 206, other input control devices 216, and external port 224. Device 200 optionally includes one or more optical sensors 264. Device 200 optionally includes one or more contact intensity sensors 265 for detecting intensity of contacts on device 200 (e.g., a touch-sensitive surface such as touch-sensitive display system 212 of device 200). Device 200 optionally includes one or more tactile output generators 267 for generating tactile outputs on device 200 (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touchpad 455 of device 400). These components optionally communicate over one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user's sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.

It should be appreciated that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in FIG. 2A are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. The computer-readable storage mediums are, for example, tangible and non-transitory. Memory 202 includes high-speed random access memory and also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 222 controls access to memory 202 by other components of device 200.

In some examples, a non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing aspects of processes described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of the server system 108 or are divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.

Peripherals interface 218 is used to couple input and output peripherals of the device to CPU 220 and memory 202. The one or more processors 220 run or execute various software programs and/or sets of instructions stored in memory 202 to perform various functions for device 200 and to process data. In some embodiments, peripherals interface 218, CPU 220, and memory controller 222 are implemented on a single chip, such as chip 204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, also called electromagnetic signals. RF circuitry 208 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 208 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 208 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 208 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11 in, and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.

Audio circuitry 210, speaker 211, and microphone 213 provide an audio interface between a user and device 200. Audio circuitry 210 receives audio data from peripherals interface 218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 211. Speaker 211 converts the electrical signal to human-audible sound waves. Audio circuitry 210 also receives electrical signals converted by microphone 213 from sound waves. Audio circuitry 210 converts the electrical signal to audio data and transmits the audio data to peripherals interface 218 for processing. Audio data are retrieved from and/or transmitted to memory 202 and/or RF circuitry 208 by peripherals interface 218. In some embodiments, audio circuitry 210 also includes a headset jack (e.g., 312, FIG. 3). The headset jack provides an interface between audio circuitry 210 and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).

I/O subsystem 206 couples input/output peripherals on device 200, such as touch screen 212 and other input control devices 216, to peripherals interface 218. I/O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. The one or more input controllers 260 receive/send electrical signals from/to other input control devices 216. The other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 260 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 308, FIG. 3) optionally include an up/down button for volume control of speaker 211 and/or microphone 213. The one or more buttons optionally include a push button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212 or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) turns power to device 200 on or off. The user is able to customize a functionality of one or more of the buttons. Touch screen 212 is used to implement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an output interface between the device and a user. Display controller 256 receives and/or sends electrical signals from/to touch screen 212. Touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 212 and display controller 256 (along with any associated modules and/or sets of instructions in memory 202) detect contact (and any movement or breaking of the contact) on touch screen 212 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 212. In an exemplary embodiment, a point of contact between touch screen 212 and the user corresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen 212 and display controller 256 detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 212. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 is analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 212 displays visual output from device 200, whereas touch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety.

Touch screen 212 has, for example, a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user makes contact with touch screen 212 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200 includes a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the various components. Power system 262 includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A shows an optical sensor coupled to optical sensor controller 258 in I/O subsystem 206. Optical sensor 264 includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor 264 receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module 243 (also called a camera module), optical sensor 264 captures still images or video. In some embodiments, an optical sensor is located on the back of device 200, opposite touch screen display 212 on the front of the device so that the touch screen display is used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user's image is obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor 264 can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor 264 is used along with the touch screen display for both video conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensity sensors 265. FIG. 2A shows a contact intensity sensor coupled to intensity sensor controller 259 in I/O subsystem 206. Contact intensity sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor 265 receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact intensity sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2A shows proximity sensor 266 coupled to peripherals interface 218. Alternately, proximity sensor 266 is coupled to input controller 260 in I/O subsystem 206. Proximity sensor 266 is performed as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen 212 when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile output generators 267. FIG. 2A shows a tactile output generator coupled to haptic feedback controller 261 in I/O subsystem 206. Tactile output generator 267 optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor 265 receives tactile feedback generation instructions from haptic feedback module 233 and generates tactile outputs on device 200 that are capable of being sensed by a user of device 200. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system 212) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device 200) or laterally (e.g., back and forth in the same plane as a surface of device 200). In some embodiments, at least one tactile output generator sensor is located on the back of device 200, opposite touch screen display 212, which is located on the front of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A shows accelerometer 268 coupled to peripherals interface 218. Alternately, accelerometer 268 is coupled to an input controller 260 in I/O subsystem 206. Accelerometer 268 performs, for example, as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device 200 optionally includes, in addition to accelerometer(s) 268, a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device 200.

In some embodiments, the software components stored in memory 202 include operating system 226, communication module (or set of instructions) 228, contact/motion module (or set of instructions) 230, graphics module (or set of instructions) 232, text input module (or set of instructions) 234, Global Positioning System (GPS) module (or set of instructions) 235, Digital Assistant Client Module 229, and applications (or sets of instructions) 236. Further, memory 202 stores data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/global internal state 257, as shown in FIGS. 2A and 4. Device/global internal state 257 includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display 212; sensor state, including information obtained from the device's various sensors and input control devices 216; and location information concerning the device's location and/or attitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

Communication module 228 facilitates communication with other devices over one or more external ports 224 and also includes various software components for handling data received by RF circuitry 208 and/or external port 224. External port 224 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen 212 (in conjunction with display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module 230 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 230 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module 230 and display controller 256 detect contact on a touchpad.

In some embodiments, contact/motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 200). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).

Contact/motion module 230 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components for rendering and displaying graphics on touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 232 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components for generating instructions used by tactile output generator(s) 267 to produce tactile outputs at one or more locations on device 200 in response to user interactions with device 200.

Text input module 234, which is, in some examples, a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module 229 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone 213, accelerometer(s) 268, touch-sensitive display system 212, optical sensor(s) 229, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker 211, touch-sensitive display system 212, tactile output generator(s) 267, etc.) of portable multifunction device 200. For example, output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models 231 include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.

In some examples, digital assistant client module 229 utilizes the various sensors, subsystems, and peripheral devices of portable multifunction device 200 to gather additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module 229 provides the contextual information or a subset thereof with the user input to DA server 106 to help infer the user's intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare and deliver outputs to the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also include the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device 200 is provided to DA server 106 as contextual information associated with a user input.

In some examples, the digital assistant client module 229 selectively provides information (e.g., user data 231) stored on the portable multifunction device 200 in response to requests from DA server 106. In some examples, digital assistant client module 229 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 passes the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user's intent expressed in the user request.

A more detailed description of a digital assistant is described below with reference to FIGS. 7A-7C. It should be recognized that digital assistant client module 229 can include any number of the sub-modules of digital assistant module 726 described below.

Applications 236 include the following modules (or sets of instructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact         list);     -   Telephone module 238;     -   Video conference module 239;     -   E-mail client module 240;     -   Instant messaging (IM) module 241;     -   Workout support module 242;     -   Camera module 243 for still and/or video images;     -   Image management module 244;     -   Video player module;     -   Music player module;     -   Browser module 247;     -   Calendar module 248;     -   Widget modules 249, which includes, in some examples, one or         more of: weather widget 249-1, stocks widget 249-2, calculator         widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,         and other widgets obtained by the user, as well as user-created         widgets 249-6;     -   Widget creator module 250 for making user-created widgets 249-6;     -   Search module 251;     -   Video and music player module 252, which merges video player         module and music player module;     -   Notes module 253;     -   Map module 254; and/or     -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, contacts module 237 are used to manage an address book or contact list (e.g., stored in application internal state 292 of contacts module 237 in memory 202 or memory 470), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone 238, video conference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, telephone module 238 are used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication uses any of a plurality of communications standards, protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact/motion module 230, graphics module 232, text input module 234, contacts module 237, and telephone module 238, video conference module 239 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, e-mail client module 240 includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module 244, e-mail client module 240 makes it very easy to create and send e-mails with still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, workout support module 242 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.

In conjunction with touch screen 212, display controller 256, optical sensor(s) 264, optical sensor controller 258, contact/motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions to capture still images or video (including a video stream) and store them into memory 202, modify characteristics of a still image or video, or delete a still image or video from memory 202.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, browser module 247 includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, e-mail client module 240, and browser module 247, calendar module 248 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, widget modules 249 are mini-applications that can be downloaded and used by a user (e.g., weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, and dictionary widget 249-5) or created by the user (e.g., user-created widget 249-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 are used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, search module 251 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 202 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 212 or on an external, connected display via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, notes module 253 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, map module 254 are used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, e-mail client module 240, and browser module 247, online video module 255 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 224), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 241, rather than e-mail client module 240, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, video player module can be combined with music player module into a single module (e.g., video and music player module 252, FIG. 2A). In some embodiments, memory 202 stores a subset of the modules and data structures identified above. Furthermore, memory 202 stores additional modules and data structures not described above.

In some embodiments, device 200 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 200, the number of physical input control devices (such as push buttons, dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 200 to a main, home, or root menu from any user interface that is displayed on device 200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., in operating system 226) and a respective application 236-1 (e.g., any of the aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines the application 236-1 and application view 291 of application 236-1 to which to deliver the event information. Event sorter 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes application internal state 292, which indicates the current application view(s) displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device/global internal state 257 is used by event sorter 270 to determine which application(s) is (are) currently active, and application internal state 292 is used by event sorter 270 to determine application views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additional information, such as one or more of: resume information to be used when application 236-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 236-1, a state queue for enabling the user to go back to a prior state or view of application 236-1, and a redo/undo queue of previous actions taken by the user.

Event monitor 271 receives event information from peripherals interface 218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 212, as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I/O subsystem 206 or a sensor, such as proximity sensor 266, accelerometer(s) 268, and/or microphone 213 (through audio circuitry 210). Information that peripherals interface 218 receives from I/O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripherals interface 218 at predetermined intervals. In response, peripherals interface 218 transmits event information. In other embodiments, peripherals interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit view determination module 272 and/or an active event recognizer determination module 273.

Hit view determination module 272 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 212 displays more than one view. Views are made up of controls and other elements that a user can see on the display.

Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is called the hit view, and the set of events that are recognized as proper inputs is determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.

Hit view determination module 272 receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 272 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.

Event dispatcher module 274 dispatches the event information to an event recognizer (e.g., event recognizer 280). In embodiments including active event recognizer determination module 273, event dispatcher module 274 delivers the event information to an event recognizer determined by active event recognizer determination module 273. In some embodiments, event dispatcher module 274 stores in an event queue the event information, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270. Alternatively, application 236-1 includes event sorter 270. In yet other embodiments, event sorter 270 is a stand-alone module, or a part of another module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view 291 of the application 236-1 includes one or more event recognizers 280. Typically, a respective application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more of event recognizers 280 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 236-1 inherits methods and other properties. In some embodiments, a respective event handler 290 includes one or more of: data updater 276, object updater 277, GUI updater 278, and/or event data 279 received from event sorter 270. Event handler 290 utilizes or calls data updater 276, object updater 277, or GUI updater 278 to update the application internal state 292. Alternatively, one or more of the application views 291 include one or more respective event handlers 290. Also, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in a respective application view 291.

A respective event recognizer 280 receives event information (e.g., event data 279) from event sorter 270 and identifies an event from the event information. Event recognizer 280 includes event receiver 282 and event comparator 284. In some embodiments, event recognizer 280 also includes at least a subset of: metadata 283, and event delivery instructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.

Event comparator 284 compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definitions 286. Event definitions 286 includes definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (287-1), event 2 (287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.

In some embodiments, event definition 287 includes a definition of an event for a respective user-interface object. In some embodiments, event comparator 284 performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with the sub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series of sub-events do not match any of the events in event definitions 286, the respective event recognizer 280 enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata 283 with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates event handler 290 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 280 delivers event information associated with the event to event handler 290. Activating an event handler 290 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 280 throws a flag associated with the recognized event, and event handler 290 associated with the flag catches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates the telephone number used in contacts module 237, or stores a video file used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates a new user-interface object or updates the position of a user-interface object. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends it to graphics module 232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of a respective application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.

It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 200 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.

FIG. 3 illustrates a portable multifunction device 200 having a touch screen 212 in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI) 300. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device 200. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 is used to navigate to any application 236 in a set of applications that is executed on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 212.

In one embodiment, device 200 includes touch screen 212, menu button 304, push button 306 for powering the device on/off and locking the device, volume adjustment button(s) 308, subscriber identity module (SIM) card slot 310, headset jack 312, and docking/charging external port 224. Push button 306 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 200 also accepts verbal input for activation or deactivation of some functions through microphone 213. Device 200 also, optionally, includes one or more contact intensity sensors 265 for detecting intensity of contacts on touch screen 212 and/or one or more tactile output generators 267 for generating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device 400 need not be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child's learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communications interfaces 460, memory 470, and one or more communication buses 420 for interconnecting these components. Communication buses 420 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device 400 includes input/output (I/O) interface 430 comprising display 440, which is typically a touch screen display. I/O interface 430 also optionally includes a keyboard and/or mouse (or other pointing device) 450 and touchpad 455, tactile output generator 457 for generating tactile outputs on device 400 (e.g., similar to tactile output generator(s) 267 described above with reference to FIG. 2A), sensors 459 (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s) 265 described above with reference to FIG. 2A). Memory 470 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 470 optionally includes one or more storage devices remotely located from CPU(s) 410. In some embodiments, memory 470 stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory 202 of portable multifunction device 200 (FIG. 2A), or a subset thereof. Furthermore, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores drawing module 480, presentation module 482, word processing module 484, website creation module 486, disk authoring module 488, and/or spreadsheet module 490, while memory 202 of portable multifunction device 200 (FIG. 2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples, stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 stores a subset of the modules and data structures identified above. Furthermore, memory 470 stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces that can be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu of applications on portable multifunction device 200 in accordance with some embodiments. Similar user interfaces are implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such as cellular and Wi-Fi signals;

-   -   Time 504;     -   Bluetooth indicator 505;     -   Battery status indicator 506;     -   Tray 508 with icons for frequently used applications, such as:         -   Icon 516 for telephone module 238, labeled “Phone,” which             optionally includes an indicator 514 of the number of missed             calls or voicemail messages;         -   Icon 518 for e-mail client module 240, labeled “Mail,” which             optionally includes an indicator 510 of the number of unread             e-mails;         -   Icon 520 for browser module 247, labeled “Browser;” and         -   Icon 522 for video and music player module 252, also             referred to as iPod (trademark of Apple Inc.) module 252,             labeled “iPod;” and     -   Icons for other applications, such as:         -   Icon 524 for IM module 241, labeled “Messages;”         -   Icon 526 for calendar module 248, labeled “Calendar;”         -   Icon 528 for image management module 244, labeled “Photos;”         -   Icon 530 for camera module 243, labeled “Camera;”         -   Icon 532 for online video module 255, labeled “Online             Video;”         -   Icon 534 for stocks widget 249-2, labeled “Stocks;”         -   Icon 536 for map module 254, labeled “Maps;”         -   Icon 538 for weather widget 249-1, labeled “Weather;”         -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”         -   Icon 542 for workout support module 242, labeled “Workout             Support;”         -   Icon 544 for notes module 253, labeled “Notes;” and         -   Icon 546 for a settings application or module, labeled             “Settings,” which provides access to settings for device 200             and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A are merely exemplary. For example, icon 522 for video and music player module 252 is optionally labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g., device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tablet or touchpad 455, FIG. 4) that is separate from the display 550 (e.g., touch screen display 212). Device 400 also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors 457) for detecting intensity of contacts on touch-sensitive surface 551 and/or one or more tactile output generators 459 for generating tactile outputs for a user of device 400.

Although some of the examples which follow will be given with reference to inputs on touch screen display 212 (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in FIG. 5B. In some embodiments, the touch-sensitive surface (e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) that corresponds to a primary axis (e.g., 553 in FIG. 5B) on the display (e.g., 550). In accordance with these embodiments, the device detects contacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface 551 at locations that correspond to respective locations on the display (e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570). In this way, user inputs (e.g., contacts 560 and 562, and movements thereof) detected by the device on the touch-sensitive surface (e.g., 551 in FIG. 5B) are used by the device to manipulate the user interface on the display (e.g., 550 in FIG. 5B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.

Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600 includes body 602. In some embodiments, device 600 includes some or all of the features described with respect to devices 200 and 400 (e.g., FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive display screen 604, hereafter touch screen 604. Alternatively, or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or the touch-sensitive surface) has one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen 604 (or the touch-sensitive surface) provide output data that represents the intensity of touches. The user interface of device 600 responds to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device 600.

Techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 600 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In some embodiments, device 600 includes some or all of the components described with respect to FIGS. 2A, 2B, and 4.

Device 600 has bus 612 that operatively couples I/O section 614 with one or more computer processors 616 and memory 618. I/O section 614 is connected to display 604, which can have touch-sensitive component 622 and, optionally, touch-intensity sensitive component 624. In addition, I/O section 614 is connected with communication unit 630 for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device 600 includes input mechanisms 606 and/or 608. Input mechanism 606 is a rotatable input device or a depressible and rotatable input device, for example. Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personal electronic device 600 includes, for example, various sensors, such as GPS sensor 632, accelerometer 634, directional sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and/or a combination thereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, cause the computer processors to perform the techniques and processes described below. The computer-executable instructions, for example, are also stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Personal electronic device 600 is not limited to the components and configuration of FIG. 6B, but can include other or additional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, for example, displayed on the display screen of devices 200, 400, 600, 1100 and/or 1300 (FIGS. 2A, 4, and 6A-B, 11, 13A-B). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) each constitutes an affordance.

As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B) while the cursor is over a particular user interface element (e.g., a button, window, slider or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212 in FIG. 5A) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user's intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds includes a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity.

The intensity of a contact on the touch-sensitive surface is characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures.

An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contact-detection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero.

In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 in accordance with various examples. In some examples, digital assistant system 700 is implemented on a standalone computer system. In some examples, digital assistant system 700 is distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant are divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices 104, 122, 200, 400, 600, 1100, or 1300) and communicates with the server portion (e.g., server system 108) through one or more networks, e.g., as shown in FIG. 1. In some examples, digital assistant system 700 is an implementation of server system 108 (and/or DA server 106) shown in FIG. 1. It should be noted that digital assistant system 700 is only one example of a digital assistant system, and that digital assistant system 700 can have more or fewer components than shown, can combine two or more components, or can have a different configuration or arrangement of the components. The various components shown in FIG. 7A are implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.

Digital assistant system 700 includes memory 702, one or more processors 704, input/output (I/O) interface 706, and network communications interface 708. These components can communicate with one another over one or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

In some examples, I/O interface 706 couples input/output devices 716 of digital assistant system 700, such as displays, keyboards, touch screens, and microphones, to user interface module 722. I/O interface 706, in conjunction with user interface module 722, receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system 700 includes any of the components and I/O communication interfaces described with respect to devices 200, 400, 600, 1100, or 1300 in FIGS. 2A, 4, 6A-6B, 11, and 13A-B, respectively. In some examples, digital assistant system 700 represents the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices 104, 200, 400, 600, 1100, or 1300).

In some examples, the network communications interface 708 includes wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) receives and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface 708 enables communication between digital assistant system 700 with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media of memory 702, stores programs, modules, instructions, and data structures including all or a subset of: operating system 718, communications module 720, user interface module 722, one or more applications 724, and digital assistant module 726. In particular, memory 702, or the computer-readable storage media of memory 702, stores instructions for performing the processes described below. One or more processors 704 execute these programs, modules, and instructions, and reads/writes from/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.

Communications module 720 facilitates communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 communicates with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in FIGS. 2A, 4, 6A-6B, respectively. Communications module 720 also includes various components for handling data received by wireless circuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a user via I/O interface 706 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module 722 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface 706 (e.g., through displays, audio channels, speakers, touch-pads, etc.).

Applications 724 include programs and/or modules that are configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, applications 724 include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system 700 is implemented on a server, applications 724 include resource management applications, diagnostic applications, or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 includes the following sub-modules, or a subset or superset thereof: input/output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726, or a subset or superset thereof: ontology 760, vocabulary index 744, user data 748, task flow models 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and models implemented in digital assistant module 726, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user's intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user's intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.

In some examples, as shown in FIG. 7B, I/O processing module 728 interacts with the user through I/O devices 716 in FIG. 7A or with a user device (e.g., devices 104, 200, 400, or 600) through network communications interface 708 in FIG. 7A to obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module 728 optionally obtains contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information includes user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module 728 also sends follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module 728 and the user request includes speech input, I/O processing module 728 forwards the speech input to STT processing module 730 (or speech recognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech pre-processor. The front-end speech pre-processor extracts representative features from the speech input. For example, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system 758 includes one or more speech recognition models (e.g., acoustic models and/or language models) and implements one or more speech recognition engines. Examples of speech recognition models include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognition results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input is processed at least partially by a third-party service or on the user's device (e.g., device 104, 200, 400, 600, 1100, or 1300) to produce the recognition result. Once STT processing module 730 produces recognition results including a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is passed to natural language processing module 732 for intent deduction. In some examples, STT processing module 730 produces multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or tokens corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence scores, STT processing module 730 ranks the candidate text representations and provides the n-best (e.g., n highest ranked) candidate text representation(s) to natural language processing module 732 for intent deduction, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is passed to natural language processing module 732 for intent deduction. In another example, the five highest ranked (n=5) candidate text representations are passed to natural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words includes a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary includes the word “tomato” that is associated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations are stored in STT processing module 730 and are associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words are determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations are manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations are ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations are associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidate pronunciation /

/ is associated with Great Britain. Further, the rank of the candidate pronunciation is based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user's profile on the device. For example, it can be determined from the user's profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation /

/ (associated with the United States) is ranked higher than the candidate pronunciation /

/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations is selected as a predicted pronunciation (e.g., the most likely pronunciation).

When a speech input is received, STT processing module 730 is used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine, based on vocabulary index 744, that this sequence corresponds to the word “tomato.”

In some examples, STT processing module 730 uses approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) of the digital assistant takes the n-best candidate text representation(s) (“word sequence(s)” or “token sequence(s)”) generated by STT processing module 730, and attempts to associate each of the candidate text representations with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” (or “user intent”) represents a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models 754. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant's capabilities is dependent on the number and variety of task flows that have been implemented and stored in task flow models 754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, also dependents on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.

In some examples, in addition to the sequence of words or tokens obtained from STT processing module 730, natural language processing module 732 also receives contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 optionally uses the contextual information to clarify, supplement, and/or further define the information included in the candidate text representations received from STT processing module 730. The contextual information includes, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like. As described herein, contextual information is, in some examples, dynamic, and changes with time, location, content of the dialogue, and other factors.

In some examples, the natural language processing is based on, e.g., ontology 760. Ontology 760 is a hierarchical structure including many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology 760 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in FIG. 7C, ontology 760 includes a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” are each directly linked to the actionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” are sub-nodes of the property node “restaurant,” and are each linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in FIG. 7C, ontology 760 also includes a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) are each linked to the “set reminder” node. Since the property “date/time” is relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” is linked to both the “restaurant reservation” node and the “set reminder” node in ontology 760.

An actionable intent node, along with its linked property nodes, is described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology 760 shown in FIG. 7C includes an example of restaurant reservation domain 762 and an example of reminder domain 764 within ontology 760. The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain 764 includes the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology 760 is made up of many domains. Each domain shares one or more property nodes with one or more other domains. For example, the “date/time” property node is associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain 762 and reminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, other domains include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain is associated with a “send a message” actionable intent node, and further includes property nodes such as “recipient(s),” “message type,” and “message body.” The property node “recipient” is further defined, for example, by the sub-property nodes such as “recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionable intents are clustered under a “super domain” in ontology 760. For example, a “travel” super-domain includes a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”

In some examples, each node in ontology 760 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node are the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node are stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to FIG. 7B, the vocabulary associated with the node for the property of “restaurant” includes words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” includes words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number,” “make a call to,” and so on. The vocabulary index 744 optionally includes words and phrases in different languages.

Natural language processing module 732 receives the candidate text representations (e.g., text string(s) or token sequence(s)) from STT processing module 730, and for each candidate representation, determines what nodes are implicated by the words in the candidate text representation. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase “triggers” or “activates” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 selects one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes is selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some examples, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.

User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default and secondary languages, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses the user-specific information to supplement the information included in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module 732 is able to access user data 748 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.

It should be recognized that in some examples, natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). In particular, the one or more machine learning mechanisms are configured to receive a candidate text representation and contextual information associated with the candidate text representation. Based on the candidate text representation and the associated contextual information, the one or more machine learning mechanisms are configured to determine intent confidence scores over a set of candidate actionable intents. Natural language processing module 732 can select one or more candidate actionable intents from the set of candidate actionable intents based on the determined intent confidence scores. In some examples, an ontology (e.g., ontology 760) is also used to select the one or more candidate actionable intents from the set of candidate actionable intents.

Other details of searching an ontology based on a token string are described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.

In some examples, once natural language processing module 732 identifies an actionable intent (or domain) based on the user request, natural language processing module 732 generates a structured query to represent the identified actionable intent. In some examples, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user says “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 is able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module 730, natural language processing module 732 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi” } and {Time=“7 pm” }. However, in this example, the user's utterance includes insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some examples, natural language processing module 732 populates some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module 732 populates a {location} parameter in the structured query with GPS coordinates from the user device.

In some examples, natural language processing module 732 identifies multiple candidate actionable intents for each candidate text representation received from STT processing module 730. Further, in some examples, a respective structured query (partial or complete) is generated for each identified candidate actionable intent. Natural language processing module 732 determines an intent confidence score for each candidate actionable intent and ranks the candidate actionable intents based on the intent confidence scores. In some examples, natural language processing module 732 passes the generated structured query (or queries), including any completed parameters, to task flow processing module 736 (“task flow processor”). In some examples, the structured query (or queries) for the m-best (e.g., m highest ranked) candidate actionable intents are provided to task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, the structured query (or queries) for the m-best candidate actionable intents are provided to task flow processing module 736 with the corresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidate actionable intents determined from multiple candidate text representations of a speech input are described in U.S. Utility application Ser. No. 14/298,725 for “System and Method for Inferring User Intent From Speech Inputs,” filed Jun. 6, 2014, the entire disclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structured query (or queries) from natural language processing module 732, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some examples, the various procedures necessary to complete these tasks are provided in task flow models 754. In some examples, task flow models 754 include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent.

As described above, in order to complete a structured query, task flow processing module 736 needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module 736 invokes dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 determines how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions are provided to and answers are received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module 734 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, dialogue flow processing module 734 then populates the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.

Once task flow processing module 736 has completed the structured query for an actionable intent, task flow processing module 736 proceeds to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module 736 executes the steps and instructions in the task flow model according to the specific parameters included in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, task flow processing module 736 performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistance of service processing module 738 (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module 738 acts on behalf of task flow processing module 736 to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service are specified by a respective service model among service models 756. Service processing module 738 accesses the appropriate service model for a service and generates requests for the service in accordance with the protocols and APIs required by the service according to the service model.

For example, if a restaurant has enabled an online reservation service, the restaurant submits a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module 736, service processing module 738 establishes a network connection with the online reservation service using the web address stored in the service model, and sends the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.

In some examples, natural language processing module 732, dialogue flow processing module 734, and task flow processing module 736 are used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent. The generated response is a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to speech synthesis processing module 740 (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response is data content relevant to satisfying a user request in the speech input.

In examples where task flow processing module 736 receives multiple structured queries from natural language processing module 732, task flow processing module 736 initially processes the first structured query of the received structured queries to attempt to complete the first structured query and/or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest ranked actionable intent. In other examples, the first structured query is selected from the received structured queries based on a combination of the corresponding speech recognition confidence scores and the corresponding intent confidence scores. In some examples, if task flow processing module 736 encounters an error during processing of the first structured query (e.g., due to an inability to determine a necessary parameter), the task flow processing module 736 can proceed to select and process a second structured query of the received structured queries that corresponds to a lower ranked actionable intent. The second structured query is selected, for example, based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate actionable intent, a missing necessary parameter in the first structured query, or any combination thereof.

Speech synthesis processing module 740 is configured to synthesize speech outputs for presentation to the user. Speech synthesis processing module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. Speech synthesis processing module 740 converts the text string to an audible speech output. Speech synthesis processing module 740 uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis processing module 740 is configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string is associated with a word in the generated dialogue response. The phonemic string is stored in metadata associated with the word. Speech synthesis processing module 740 is configured to directly process the phonemic string in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., the server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it is possible to obtain higher quality speech outputs than would be practical with client-side synthesis.

Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference.

4. Exemplary Functions and Architectures of a Digital Assistant Providing Improved Detection and Correction of Typographical Errors.

FIG. 8 illustrates a block diagram of a digital assistant 800 for generating a typographical error model, according to various examples. In some examples, digital assistant 800 (e.g., digital assistant system 700) is implemented by a user device according to various examples. In some examples, the user device, a server (e.g., server 108), or a combination thereof, can implement digital assistant 800. The user device can be implemented using, for example, device 104, 200, 400, 600, 1100, or 1300 as illustrated in FIGS. 1, 2A-2B, 4, 6A-6B, 11, and 13A-13B. In some examples, digital assistant 800 can be implemented using digital assistant module 726 of digital assistant system 700. Digital assistant 800 includes one or more modules, models, applications, vocabularies, and user data similar to those of digital assistant module 726. For example, digital assistant 800 includes the following sub-modules, or a subset or superset thereof: an input/output processing module, an STT process module, a natural language processing module, a task flow processing module, and a speech synthesis module. These modules can also be implemented similar to that of the corresponding modules as illustrated in FIG. 7B, and therefore are not shown and not repeatedly described.

As illustrated in FIG. 8, in some examples, digital assistant 800 can include a seed data extractor 820, a learning network 830, and a learning network 880. Learning network 830 can include, for example, a generator 840 and a discriminator 860 for performing unsupervised training. As illustrated in FIG. 8, digital assistant 800 can receive one or more input words 802, which may or may not include typographical errors. In some examples, a typographical error can be a non-atomic typographical error or an atomic typographical error. A word having a non-atomic typographical error is lexically incorrect (e.g., “moove” instead of “move” or “bakler” instead of “baker”). A word that is lexically incorrect can include extra characters, have missing characters, and/or have incorrectly-positioned characters (e.g., two characters being swapped, shifted, etc.). In some examples, a character includes a letter, a number, a punctuation mark, or the absence of a letter (e.g., a space). A word having an atomic typographical error is lexically correct but contextually incorrect. A word that is contextually incorrect can include words that have incorrect usage, tense, grammar, or the like. For example, the word “prostate” is used in the sentence “the prostate figure on the ground” when the word “prostrate” is actually intended; or the word “nuclear” is used in a sentence “your answer is nuclear” when the word “unclear” is actually intended.

In some examples, input words 802 can be provided by a user, by a computer, and/or by an application (e.g., a dictionary application, a message application). Input words 802 can be provided by one particular user, by multiple different users, or by a group of users. Input words 802 can be customized (e.g., for a particular user, a group of users, etc.) to include words commonly used by that specific user or group of users. Input words 802 can also be customized to include words commonly used by a general population. Further, input words 802 can be customized to add words associated with a specific user or group of users, even if the words are not commonly or frequently used.

In some examples, one or more input words 802 can include a single word, a plurality of words of a sentence, a paragraph, or any other structure that provides context about the input words. In some examples, one or more input words 802 can include training data 824 which, as discussed further below, is a collection of words having typographical errors made by human users. Thus, in some examples, input words 802 can include words that are absent typographical errors and words that include typographical errors. In some examples, input words 802 can be provided to seed data extractor 820.

FIG. 9 illustrates a block diagram of a seed data extractor 820 according to various examples. As illustrated in FIG. 9, seed data extractor 820 can extract seed data 822 based on one or more input words 802. In some embodiments, seed data extractor 820 includes an input layer 910, a first recurrent neural network layer 920, an aggregating layer 930, a second neural network layer 940, and a second aggregating layer 950. It is appreciated that seed data extractor 820 can include one or more additional recurrent neural network layers and/or one or more additional aggregating layers. Those additional layers are not shown in FIG. 9. In some examples, input layer 910 of seed data extractor 820 can include, for example, a character sequence generator (e.g., a tokenizer, not shown) to generate an input character sequence based on one or more input words 802.

As shown in FIG. 9, the input character sequence includes a plurality of characters represented by one or more vectors (e.g., vector 912 corresponding to c_(k)). The one or more characters (e.g., represented by vectors denoted by c₁, c₂, . . . c_(K)) of the input character sequence can be a portion of the obtained input words (e.g., one or more input words 802). In some examples, input layer 910 of seed data extractor 820 can include an encoder (not shown) to encode one or more characters of the input character sequence as a vector (e.g., vector 912 denoted by c_(k)) having a dimension of N, where “N” represents the total number of distinct characters in a pre-determined character collection. For example, if a character of the input character sequence is an English language character, the dimension of N can be 26 (if lower case and upper case are not differentiated) because there are total of 26 letters in the English alphabet. As another example, the dimension of N can be 27 if an end-of-word symbol </w> is included in the pre-determined character collection. As a result, the encoder can encode a character (e.g., represented by vector 912 corresponding to c_(k)) as a vector having a 1-of-N encoding. In some examples, the vector can be represented by a sparse vector that has a dimension of N (e.g., a vector that has one non-zero element and all other elements having a value of zero, also referred to as a one hot vector). In some examples, the vector can be represented by a dense vector with a predetermined dimension (e.g., when the vector is associated with pre-trained character embedding).

With reference to FIG. 9, for the plurality of vectors corresponding to c₁, c₂, c_(k), . . . c_(K), the lower case letter “k” of c_(k) denotes the “k-th” character in the input character sequence and the upper case letter “K” of c_(K) denotes the maximum number of characters in the character sequence being processed. Thus, because the maximum number of characters for any given input words 802 can vary, the length of input character sequence varies and therefore, the number of vectors representing the characters in the input character sequence also varies. In some examples, the total number of vectors representing the characters can be large. As described in more detail below, the seed data extractor 820 can generate a fixed-length seed data 822 for the unsupervised training of learning network 830 (e.g., a GAN). Thus, learning network 830 receives fixed-length vectors as seed data based on encoding of the input character sequence, despite that the length of the input character sequence obtained from input words 802 may vary widely. In some examples, the input character sequence can also include an end-of-word symbol (e.g., </w>) representing the end of input words 802. As described below, using a character sequence as input to first recurrent neural network layer 920 for processing any particular character (e.g., a character being currently processed, represented by vector c_(k)) can generate seed data 822 that are encoded with context of the particular character. As a result, the unsupervised training of learning network 830 is also performed with contextual data encoded in seed data 822. As described above, in natural language processing, contextual data can be used to determine the lexical and/or contextual correctness of a word (e.g., the correct spelling and/or position of a character/word). Consequently, the unsupervised training of learning network 830 described in more detail below factors in the contextual data in generating a data set for subsequent supervised training.

As illustrated in FIG. 9, in some examples, the encoded input character sequence (e.g., represented by vectors denoted by c₁, c₂, . . . c_(k) . . . c_(K)) can be provided to a first stage of extracting and aggregation including first recurrent neural network layer 920 and aggregating layer 930. In particular, the encoded input character sequence is provided to first recurrent neural network layer 920. In some examples, first recurrent neural network layer 920 of seed data extractor 820 can include a recurrent neural network (RNN) (e.g., a long short-term memory (LSTM) network). An LSTM network can be, for example, a bi-direction LSTM. An LSTM network includes a plurality of LSTM cells (shown in FIG. 9) and can include an input layer, an output layer, and one or more hidden layers in each of the LSTM cells. In a bi-directional LSTM network, the output of a current time step can be determined based on two versions of context: a preceding context and a following context. In some examples, a preceding context represents output values in a hidden layer from a previous time step with respect to a current character or word being processed in a current time step; and a following context represents output values in a hidden layer from a future time step with respect to a current character or word being processed in a current time step. The preceding context is sometimes also referred to as left context, and the following context is sometimes also referred to as right context.

With reference to FIG. 9, first recurrent neural network layer 920 (e.g., a bi-directional LSTM network) generates a plurality of first interim vectors (e.g., denoted by s_(k) and r_(k)) encoded with the contextual data associated with the one or more input words 802. In some examples, to generate the first interim vectors, first recurrent neural network layer 920 determines, for a current character (e.g., represented by a vector denoted by c_(k)), a first interim vector 922 (e.g., denoted by s_(k)) representing the preceding context of the current character. First interim vector 922 (e.g., denoted by s_(k)) can be determined based on formula (1) below.

s _(k) =T{W _(SC) ·c _(k) +W _(SS) ·s _(k−1)}  (1)

where 922 first interim vector (e.g., denoted by s_(k)) is determined based on a preceding first interim vector (e.g., denoted by s_(k−1)) representing the preceding context at a preceding time step and a vector representing a current character (e.g., c_(k)). In formula (1), c_(k) denotes a vector 912 representing a current character for a current time step; s_(k−1) denotes a preceding first interim vector representing the preceding context at a preceding time step; and W_(SC) and W_(SS) denote weight matrices of compatible dimensions. In some examples, W_(SC) and W_(SS) can be updated during the training of the LSTM network in first recurrent neural network layer 920. In formula (1), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a preceding first interim vector (e.g., s_(k−1)) is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) associated with hidden nodes in the hidden layer of the LSTM network in first recurrent neural network layer 920. In some examples, a preceding first interim vector (e.g., denoted by s_(k−1)) has a dimension of H. In some examples, a preceding first interim vector may be a dense vector. First interim vector 922 (e.g., denoted by s_(k)) representing the preceding context is an internal representation of preceding context as an output of a current time step of the LSTM network in first recurrent neural network layer 920 (e.g., output values of preceding hidden nodes of the hidden layer of an LSTM network in first recurrent neural network layer 920 at a current time step).

In some examples, to generate the first interim vectors, first recurrent neural network layer 920 also determines, for a current character (e.g., represented by a vector denoted by c_(k)), another first interim vector 924 (e.g., denoted by r_(k)) representing the following context of the current character. First interim vector 924 (e.g., denoted by r_(k)) can be determined based on formula (2) below.

r _(k) =T{W _(RC) ·c _(k) +W _(RR) ·r _(k+1)}  (2)

where first interim vector 924 (e.g., denoted by r_(k)) is determined based on a following first interim vector (e.g., denoted by r_(k+1)) representing the following context at a following time step and a vector representing a current character (e.g., c_(k)). In formula (2), c_(k) denotes a vector 912 representing a current character for a current time step; r_(k+1) denotes a following first interim vector representing the following context at a following time step (a future time step); and W_(RC) and W_(RR) denote weight matrices of compatible dimensions. In some examples, W_(RC) and W_(RR) can be updated during the training of the LSTM network in first recurrent neural network layer 920. In formula (2), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a following first interim vector (e.g., denoted by r_(k+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) associated with hidden nodes in the hidden layer of the LSTM network in first recurrent neural network layer 920. A following first interim vector (e.g., denoted by r_(k+1)) can have a dimension of H. In some examples, a following first interim vector may be a dense vector. First interim vector 924 (e.g., denoted by r_(k)) representing the following context is an internal representation of following context as an output of a current time step of the LSTM network in first recurrent neural network layer 920 (e.g., the output values of following hidden nodes of the hidden layer of an LSTM network in first recurrent neural network layer 920 at a current time step).

In some examples, the encoded contextual information represents one or more characters preceding or following the current character (e.g., represented by vector 912 denoted by c_(k)). In some examples, the encoded contextual information represents one or more words preceding or following the current character (e.g., represented by vector 912 denoted by c_(k)).

As illustrated in FIG. 9, the plurality of first interim vectors (e.g., denoted by s₁, s₂ . . . s_(k), s_(k+1) . . . s_(K+1) and r₁, r₂, . . . r_(k), r_(k+1) . . . r_(K+1)) are aggregated by aggregating layer 930. In some examples, aggregating the plurality of first interim vectors includes an average pooling. Average pooling performs, for example, down-sampling by dividing the first interim vectors into pooling regions and computing the average values of each region. Average pooling in this way increases the effective modeling span by distilling large sets of data into a finite vector. In some examples, aggregating the plurality of first interim vectors can include other types of pooling, such as maximum pooling.

With reference to FIG. 9, in some examples, aggregating layer 930 can include a forward average pooling stage and a backward average pooling stage. In some examples, aggregating the plurality of third interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In the forward average pooling stage, one or more subsets of first interim vectors (e.g., denoted by s₁, s₂ . . . s_(k), s_(k+1) . . . s_(K+1)) representing the preceding context are aggregated to generate a plurality of second interim vectors (e.g., a second interim vector 932 denoted by f_(p)). In some examples, the forward average pooling stage generates a plurality of second interim vectors including a second interim vector 932 (e.g., f_(p)) according to formula (3) below.

$\begin{matrix} {f_{p} = {\frac{1}{\mathcal{I}_{p}}{\sum\limits_{k\; \epsilon \; I_{p}}s_{k}}}} & (3) \end{matrix}$

where, as described with respect to formula (1) above, s_(k) denotes a first interim vector representing the preceding context of the current character; and I_(p) denotes the pth instance of P non-overlapping subsets of [1 . . . K], each associated with a span of approximately [K/P] characters.

With reference to FIG. 9, as described above, aggregating layer 930 can also include, for example, a backward average pooling stage. In the backward average pooling stage, one or more subsets of first interim vectors (e.g., r₁, r₂, . . . r_(k), r_(k+1), . . . r_(K)) representing the following context are aggregated to generate a plurality of second interim vectors (e.g., second interim vector 934 denoted by b_(p)). In some examples, the backward average pooling layer generates a plurality of second interim vectors including a second interim vector 934 (e.g., b_(p)) according to formula (4) below.

$\begin{matrix} {b_{p} = {\frac{1}{\mathcal{I}_{p}}{\sum\limits_{k\; \epsilon \; \mathcal{I}_{p}}r_{k}}}} & (4) \end{matrix}$

where, as described with respect to formula (2) above, r_(k) denotes a first interim vector 924 representing the following context of the current character; and I_(p) denotes the pth instance of P non-overlapping subsets of [1 . . . K], each associated with a span of approximately [K/P] characters. In some examples, because of the pooling operations, the total number of second interim vectors (e.g., vectors 932 and 934) is less than that of the first interim vectors (e.g., vectors 922 and 924).

As illustrated in FIG. 9, in some examples, the second interim vectors (e.g., vectors 932 and 934) are then provided to a second stage of extracting and aggregating, which can include a second recurrent neural network layer 940 and a second aggregating layer 950. The second stage of extracting and aggregating can operate substantially similar to the first stage of extracting and aggregating, as described above. In some examples, as a result of the aggregating operation performed in the first stage of extracting and aggregating, the second stage of extracting and aggregating include less number of RNN cells or elements in second recurrent neural network layer 940 and less number of pooling elements in second aggregating layer 950. In some examples, the plurality of second interim vectors (e.g., vectors 932 and 934) are provided to second recurrent neural network layer 940. Second recurrent neural network layer 940 can also include a RNN. In particular, second recurrent neural network layer 940 can include, for example, a bi-direction LSTM with two versions of context: preceding context and following context.

As shown in FIG. 9, second recurrent neural network layer 940 generates a plurality of third interim vectors based on the plurality of second interim vectors. In some examples, to generate the third interim vectors, second recurrent neural network layer 940 determines, for a second interim vector 932 (e.g., denoted by f_(p)), a third interim vector 942 (e.g., denoted by u_(p)) representing the preceding context of the second interim vector 932. Third interim vector 942 (e.g., denoted by u_(p)) can be determined according to formula (5) below.

u _(p) =T{W _(UF) ·f _(p) +W _(UU) ·u _(p−1)}  (5)

where third interim vector 942 (e.g., denoted by u_(p)) is determined based on a preceding third interim vector (e.g., denoted by u_(p−1)) representing the preceding context at a preceding time step and a second interim vector 932 (e.g., f_(p)). In formula (5), f_(p) denotes second interim vector 932 at a current time step; u_(p−1) denotes a preceding third interim vector representing the preceding context at a previous time step (a past time step); and W_(UF) and W_(UU) denote weight matrices of compatible dimensions. In some examples, W_(UF) and W_(UU) can be updated during the training of the LSTM network in second recurrent neural network layer 940. In formula (5), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a preceding third interim vector (e.g., denoted by u_(p−1)) is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) associated with hidden nodes in the hidden layer of the LSTM network in second recurrent neural network layer 940. A preceding third interim vector (e.g., denoted by u_(p−1)) can have a dimension of H. In some examples, a preceding third interim vector may be a dense vector. Third interim vector 942 (e.g., denoted by u_(p)) representing the preceding context is an internal representation of preceding context as an output of a current time step of the LSTM network in second recurrent neural network layer 940 (e.g., the output values of preceding hidden nodes of the hidden layer of an LSTM network in second recurrent neural network layer 940 at a current time step).

In some examples, to generate the third interim vectors, second recurrent neural network layer 940 also determines, for a second interim vector 934 (e.g., denoted by b_(p)), a third interim vector 944 (e.g., denoted by v_(p)) representing the following context of second interim vector 934. In some examples, third interim vector 944 (e.g., denoted by v_(p)) representing the following context can be generated according to formula (6) below.

v _(p) =T{W _(VB) ·b _(p) +W _(VV) ·v _(p+1)}  (6)

where third interim vector 944 (e.g., denoted by v_(p)) is determined based on a following third interim vector (e.g., denoted by v_(p+1)) representing the following context at a following time step and a second interim vector 934 (e.g., denoted by b_(p)). In formula (6), b_(p) denotes a second interim vector 934 at a current time step; v_(p+1) denotes a following third interim vector representing the following context at a following time step; W_(VB) and W_(VV) denote weight matrices of compatible dimensions. In some examples, W_(VB) and W_(VV) can be updated during the training of the LSTM network in second recurrent neural network layer 940. In formula (6), T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof.

In some examples, a following third interim vector (e.g., denoted by v_(p+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) associated with hidden nodes in the hidden layer of the LSTM network in second recurrent neural network layer 940. A following third interim vector (e.g., denoted by v_(p+1)) can have a dimension of H. In some examples, a following third interim vector may be a dense vector. Third interim vector 944 (e.g., denoted by v_(p)) representing the following context is an internal representation of following context as an output of a current time step of the LSTM network in second recurrent neural network layer 940 (e.g., the output values of following hidden nodes of the hidden layer of an LSTM network in second recurrent neural network layer 940 at a current time step).

As illustrated in FIG. 9, similar to those described above, second aggregating layer 950 can include a forward average pooling stage and a backward average pooling stage, for aggregating the plurality of third interim vectors (e.g., denoted by u₁, u₂ . . . u_(p) and v₁, v₂, . . . v_(p)). In some examples, aggregating the plurality of third interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In the forwarding average pooling stage, one or more subsets of third interim vectors (e.g., u₁, u₂, . . . u_(p), u_(p+1), . . . u_(P)) representing the preceding context are aggregated to generate a fourth interim vector 952 (e.g., denoted by ū). In some examples, the forward average pooling stage generates a plurality of fourth interim vectors including fourth interim vector 952 (e.g., denoted by ū) according to formula (7) below.

$\begin{matrix} {\overset{\_}{u} = {\frac{1}{P}{\sum\limits_{p}u_{p}}}} & (7) \end{matrix}$

where, u_(p) denotes the plurality of third interim vectors representing the preceding context; and P represents the number of non-overlapping subsets of [1 . . . K]. Similar to described above, because of the pooling operation in the second aggregating layer 950, the total number of fourth interim vectors (e.g., vector 952) is less than that of the third interim vectors (e.g., vector 942). In some examples, fourth interim vector 952 (e.g., denoted by u′) has a dimension of H and can be a dense vector.

As described above, second aggregating layer 950 can also include a backward average pooling stage, for aggregating the plurality of third interim vectors (e.g., denoted by v₁, v₂, . . . v_(p)) representing the following context. In some examples, aggregating the plurality of third interim vectors includes an average pooling, a maximum pooling, or any other pooling algorithms. In some examples, the backward average pooling stage generates a plurality of fourth interim vectors including a fourth interim vector 954 (e.g., denoted by v) according to formula (8) below.

$\begin{matrix} {\overset{\_}{v} = {\frac{1}{P}{\sum\limits_{p}v_{p}}}} & (8) \end{matrix}$

where, v_(p) denotes the plurality of third interim vectors representing the following context; and P represents the number of non-overlapping subsets of [1 . . . K]. Similar to described above, because of the pooling operation in the second aggregating layer 950, the total number of fourth interim vectors (e.g., vector 954) is less than that of third interim vectors (e.g., vector 944). In the example illustrated in FIG. 9, two fourth interim vectors (vectors 952 and 954) are shown. It is appreciated, however, that any number of fourth interim vectors may be generated. And as described below, any number of additional stages of extracting and aggregating can be included in seed data extractor 820. In some examples, fourth interim vector 954 (e.g., denoted by v) has a dimension of H.

As illustrated in FIG. 9, in some examples, fourth interim vectors 952 and 954 are concatenated to generate a vector representing at least a portion of seed data 822 (e.g., a vector denoted by z). For example, a single vector in seed data 822 can be generated according to z=[ū v]. In some examples, a vector of seed data 822 (e.g., vector denoted by z) has dimension 2H. Thus, rather than using a vector of varying dimensions (e.g., due to the varying length of words), a vector of seed data 822 provides a fixed-length vector (e.g., having dimension 2H) for unsupervised training of learning network 830. This is desirable for performing the unsupervised training because the efficiency of the learning network can be improved (e.g., faster convergence and predictable seed input).

As described below, seed data 822 can be used for unsupervised training of learning network 830 to generate output words for subsequent supervised training of learning network 880. In some examples, one or more vectors (or the words represented by the vectors) in seed data 822 can be perturbed and used for unsupervised training of learning network 830. The perturbed seed data 822 can generate additional output words for subsequent supervised training of learning network 880.

While the example provided in FIG. 9 illustrates two extracting and aggregating stages, it is appreciated that any number of extracting and aggregating stages may be used to generate the desired seed data 822 (e.g., denoted by z). For example, seed data extractor 820 could include three, four, five, six, etc. extracting and aggregating stages. By increasing the number of stages the encoded context information can be aggregated over increasingly large spans, increasing the effective modeling span of contextual data (e.g., from one or more characters in a word to the entire character sequence of one or more words).

Returning to FIG. 8, seed data extractor 820 provides seed data 822 to learning network 830 to perform unsupervised learning. In some examples, learning network 830 includes a generative adversarial network (GAN). A generative adversarial network is type of learning network used in unsupervised machine learning and can include a generator 840 and a discriminator 860 contesting each other in a zero-sum game framework. In particular, learning network 830 is trained to generate one or more output words that include typographical errors based on seed data 822 and training data 824. In some examples, an unsupervised training is performed for learning network 830 to generate output words, which can include words having non-atomic typographical errors and words having atomic typographical errors. As described in more detail below, the output words determined by learning network 830 can have a probability distribution corresponding to a probability distribution of the training data 824, which is a collection of words having typographical errors made by human users.

As previously discussed, in some examples, training data 824 is included in one or more input words 802. In some examples, training data 824 includes a plurality of words having typographical errors collected from a plurality of users. In some examples, each word of training data 824 includes a typographical error made by one or more of the plurality of users, and are thus errors actually made by human users. Training data 824 can be collected from the plurality of users by, for example, collecting words including typographical errors made by users in various applications (e.g., text editing applications, text messaging applications, email applications, etc.). As described above, collecting words that have all conceivable typographical errors (e.g., all possible permutation of letters in a word, all possible misspellings of a word) is impractical and often time impossible. Thus, in some examples, training data 824 can be a limited collection of words having typographical errors that are made by human users (one user, a group of users, or a general population).

In some examples, the limited collection of words can include words that have representative types of typographic errors. For example, for a particular word, training data 824 can include commonly-made errors such as an extra letter (e.g., “moove” instead of “move”), a missing letter (e.g., “imperonating” instead of “impersonating”), a letter that is placed in a wrong position (e.g., “teh” instead of “the”), a commonly-made atomic error (e.g., “principle” and “principal”). It is appreciated that training data 824 can be customized to any single user, a group of users, or a general population of users such that it includes representative errors that are frequently-made or commonly-made. Training data 824, however, does not include all possible or conceivable errors. And as described in more detail below, the unsupervised training of learning network 830 can, using training data 824 and seed data 822, determine additional words that have typographical errors that resemble those in training data 824. Thus, learning network 830 can generate output words having realistic typographic errors and therefore provide a more efficient way to subsequently generate a more accurate and intelligent typographical error model.

FIG. 10 illustrates a block diagram of learning network 830 for generating a data set 832 including output words having typographical errors through unsupervised training. As discussed above, learning network 830 can include as a generative adversarial network having a generator 840 and a discriminator 860. As illustrated by FIG. 10, in some examples, generator 840 receives seed data 822 (e.g., denoted by z). As described above, seed data 822 consists of a fixed-length vector (e.g., a vector denoted by z, also referred to as a seed, a seed vector, or a word-specific seed generated from one or more input words and/or one or more perturbed vectors or seeds). A seed (e.g., denoted by z) correspond to a particular input word of one or more input words 802. As described above, the vector of a seed can have a dimension of 2H.

In some examples, generator 840 can include a unidirectional LSTM, which is a type of RNN. A unidirectional LSTM network can also include an input layer, one or more hidden layers, and an output layer. An input layer in the unidirectional LSTM receives a seed vector; the one or more hidden layers provide one or more state vectors (e.g., denoted by g_(m)) for factoring in contextual data; and the output layer generates output vectors using the state vectors. A state vector in a unidirectional LSTM includes a preceding context of a current time step but not a following context of the current time step. In some examples, a preceding context is also referred to as a left context of a current character or word for a current time step.

In some examples, one or more hidden layers of generator 840 determine, for a current time step, a state vector (e.g., denoted by g_(m)) representing the preceding context. In some examples, the state vector for a current time step (e.g., denoted by g_(m)) can be generated according to formula (9) below.

g _(m) =T{W _(GZ) ·z+W _(GG) ·g _(m−1)}  (9)

where the state vector g_(m) is generated based on a preceding state vector at a preceding time step (e.g., denoted by g_(m−1)) and a seed vector (e.g., denoted by z). In formula (9), z denotes the seed vector; g_(m−1) denotes the preceding state vector representing the preceding context at a preceding time step; and W_(GZ) and W_(GG) denote weight matrices with compatible dimensions. In some examples, W_(GZ) and W_(GG) can be updated during the training of learning network 830 (e.g., after each feedback received from discriminator 860). In some embodiments, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a state vector representing the preceding context at the current time step (e.g., g_(m)) has a dimension of 2H.

A preceding state vector representing the preceding context (e.g., denoted by g_(m−1)) includes an internal representation of context from one or more output values at a preceding time step (e.g., a past time step) in the hidden layer of the LSTM network in generator 840. A current stated vector representing the preceding context (e.g., denoted by g_(m)) includes an internal representation of context from one or more output values at a current time step in the hidden layer of the LSTM network in generator 840.

With reference to FIG. 10, generator 840 generates, based on the current vector representing the preceding context (e.g., denoted by g_(m)), one or more generator-output vectors representing a generated character sequence (e.g., a generator-output vector 1012 denoted by c_(m)). The generated character sequence represents a word having one or more typographical errors. In some examples, a generator-output vector (e.g., denoted by c_(m)) represents a character and can be generated using the formula (10) below.

c _(m) =S{W _(CG) ·g _(m)}  (10)

where, g_(m) represents a state vector representing the preceding context at a current time step; and W_(CG) represents a weight matrix with compatible dimensions. W_(CG) can be updated during unsupervised training of learning network 830. In some examples, S{ } denotes a softmax activation function. In some examples, a generator-output vector representing a character (e.g., denoted by c_(m)) in the generated character sequence has a 1-of-N encoding (e.g., similar to the encoding of characters in the input words 802) and thus has a dimension of N.

Similar to described above, N represents the total number of distinct characters in a pre-determined character collection. In some examples, a generator-output vector representing a character (e.g., denoted by c_(m)) is measured over the span [1 . . . (M+1)], where M is the total number of characters in the generated character sequence. For example, for a generated character sequence such as “moove,” M is 5. In some examples, the generator-output vectors representing the generated character sequence includes a vector representing an end-of-word symbol </w>, which represents the end of the generated character sequence. In some examples, the end-of-symbol </w> corresponds to (M+1) character in the generated character sequence. In some examples, the number M associated with the generated character sequence does not necessarily equal to the number K associated with an input word of input words 802. As described above, the generated character sequence generated by generator 840 represents a word having typographical errors given the input word absent of typographical errors. A typographic error may have missing characters, extra characters, or characters in an incorrect position. Therefore, the total number of characters in the generated character sequence generated by generator 840 (e.g., M) can be different from the total number of characters in the input word (e.g., K), depending on the type of error.

With reference to FIG. 10, the generator-output vectors (e.g., vector 1012 denoted by c_(m)) representing the generated character sequence are then provided to discriminator 860. In some examples, discriminator 860 can include RNN, for example, a bi-direction LSTM. Discriminator 860 determines, for a generator-output vector (e.g., vector 1012 denoted by c_(m)), a state vector (e.g., denoted by [d_(m) e_(m)]) having a dimension of 2H. In some examples, the state vector can include a first state vector (e.g., denoted by d_(m)) representing the preceding context (also referred to as left context) and a second state vector (e.g., denoted by c_(m)) representing the following context (also referred to as right context). In some examples, a first state vector (e.g., denoted by d_(m)) representing the preceding context can be determined according to formula (11) below.

d _(m) =T{W _(DC) ·c _(m) +W _(DD) ·d _(m−1)}  (11)

where the first state vector (e.g., denoted by d_(m)) representing the preceding context is generated based on a preceding first state vector representing the preceding context at a previous time step (e.g., denoted by d_(m−1)) and a generator-output vector (e.g., vector 1012 denoted by c_(m)). In formula (11), c_(m) denotes a generator-output vector (e.g., vector 1012); d_(m−1) denotes the preceding state vector representing the preceding context; and W_(DC) and W_(DD) denote weight matrices of compatible dimensions. W_(DC) and W_(DD) can be updated during the unsupervised training of learning network 830. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a first state vector representing the preceding context (e.g., denoted by d_(m)) has a dimension of H.

Similarly to the vectors generated by the other LSTM networks described herein, a preceding first state vector (e.g., denoted by d_(m−1)) representing the preceding context at a previous time step is an internal representation of context from one or more output values of a preceding time step (e.g., a past time step) in the hidden layer of the LSTM network in discriminator 860. Likewise, a first state vector (e.g., denoted by d_(m)) representing the preceding context at a current time step is an internal representation of preceding context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 860.

In some examples, discriminator 860 determines, for a generator-output vector (e.g., vector 1012 denoted by c_(m)), a second state vector (e.g., denoted by e_(m)) representing the following context. In some examples, a second state vector (e.g., denoted by e_(m)) representing the following context can be determined according to formula (12) below.

e _(m) =T{W _(EC) ·c _(m) +W _(EE) ·e _(m+1)}  (12)

where the second state vector (e.g., denoted by e_(m)) representing the following context is determined based on a following second state vector representing the following context at a following time step (e.g., denoted by e_(m+1)) and the generator-output vector at a current time step (e.g., vector 1012 denoted by c_(m)). In formula (12), c_(m) denotes the generator-output vector at a current time step (e.g., vector 1012); e_(m+1) denotes the following second state vector representing the following context at a following time step (a future time step); and W_(EC) and W_(EE) denote weight matrices of compatible dimensions. W_(EC) and W_(EE) can be updated during the unsupervised training of discriminator 860. In some examples, T { } denotes an activation function, such as a sigmoid, a hyperbolic tangent, rectified linear unit, any function related thereto, or any combination thereof. In some examples, a second state vector representing the following context (e.g., denoted by e_(m)) at a current time step can have a dimension of H.

Similarly to those described above, a following second state vector representing the following context (e.g., denoted by e_(m+1)) is an internal representation of context from one or more output values of a following time step (e.g., a future time step) in the hidden layer of the LSTM network in discriminator 860. Likewise, a second state vector (e.g., denoted by e_(m)) representing the following context is an internal representation of following context from one or more output values of a current time step in the hidden layer of the LSTM network in discriminator 860.

In some examples, discriminator 860 then determines a discriminator-output vector 1022 (e.g., denoted by q′) indicating the probability that the one or more output words (e.g., represented by the plurality of generator-output vectors denoted by c₁ . . . c_(m) . . . c_(M+1)) having a probability distribution corresponding to the probability distribution of the training data 824. In some examples, the training data 824 can be used to configure the initial state of the discriminator 860 (e.g., configure the initial weights of an LSTM network of discriminator 860). As described above, training data 824 includes a limited collection of words having typographical errors made by human users. Therefore, the probability distribution of training data 824 is determined, for example, at the time discriminator 860 is configured initially. During the training of discriminator 860, the probability distribution of the generator-output vectors representing the generated character sequence is also determined and compared to the probability distribution of training data 824. The result of the comparison is thus the probability that the generator generated character sequence (e.g., a generator-generated word having typographic errors) is a typographical word that is associated with the corresponding input word. In some examples, the discriminator-output vector 1022 (e.g., denoted by q′) is generated according to formula (13) below.

q′=S{W _(YD)·[d _(M+1) e ₁]}  (13)

where the discriminator-output vector 1022 (e.g., q′) is generated after all the generator-output vectors representing the generated character sequence are processed by the LSTM network in discriminator 860. In some examples, the discriminator-output vector 1022 is encoded as a binary vector based on the (M+1)th first state vector (e.g., denoted by d_(M+1)) representing all the preceding context and the 1st second state vectors (e.g., e₁) representing all the following context. In formula (13), d_(M+1) denotes the last state vector representing the preceding context; e₁ denotes the first state vector representing the following context; and W_(YD) denotes a weight matrix of compatible dimensions. W_(YD) can be updated during training of discriminator 860. By using d_(M+1), all of the preceding context is encoded into the discriminator-output vector 1022 (e.g., q′) because d_(M+1), as the last state vector representing the preceding context, considers the context for all preceding time-steps. Similarly, by using e₁, all of the following context is encoded into the discriminator-output vector 1022 (e.g., q′) because e₁, as the first state vector representing following context, considers the context for all following time-steps. In some examples, S { } denotes a softmax activation function.

As described above, the discriminator-output vector 1022 (e.g., q′) conveys the probability that the one or more output words represented by the generator-output vectors (e.g., denoted by c₁ . . . c_(m) . . . c_(M+1)), is a word including one or more typographical errors associated with the one or more input words 802. In some examples, the first generator-generated character sequence (e.g., represented by the plurality of generator-output vectors denoted by c₁ . . . c_(m) . . . c_(M+1)), which is generated by generator 840 based on the seed data 822, may not be considered a valid word including one or more typographical errors associated with the one or more input words 802. That is, when the first plurality of generator-output vectors is provided to discriminator 860, the probability distribution of the generator-generated character sequence is likely dissimilar or does not match the probability distribution of the training data 824 (e.g., including a collection of words having typographical errors made by human users). In other words, the probability indicated discriminator-output vector 1022 (e.g., q′) is likely low (e.g., near zero).

Accordingly, in some examples, learning network 830 (e.g., the GAN) is trained in an unsupervised manner by allowing the learning network 830 to iteratively generate generator-output vectors and determine the discriminator-output vector 1022. In each iteration, generator 840 receives feedback (e.g., based on discriminator-output vector 1022 denoted by q′) and updates at least one of the parameters (e.g., weight matrixes) of generator 840 and the seed data (e.g., perturbing the seed data). Similarly, in each iteration, discriminator 860 receives a generator-generated character sequence and updates the parameters of the discriminator 860. This iterative, unsupervised training process is continued until the discriminator-output vector 1022 approaches a predetermined threshold, indicating that the generated character sequence (represented by the plurality of generator-output vectors) corresponds to a realistic word with a typographical error (e.g., errors appear to be made by a human user but are actually made by the generator 840). In some examples, the unsupervised training converges in a linear or nonlinear manner.

In some examples, the unsupervised training of learning network 830 is determined to be complete when a cost function representing the learning network 830 converges. In particular, the GAN iterative framework of learning network 830 can be modeled as a minimax cost function, in which the generator 840 (e.g., denoted by

) and the discriminator 860 (e.g., denoted by

) are jointly trained in an unsupervised manner by jointly solving formula 14 below.

$\begin{matrix} {{\min_{}{\max_{}{\left( {,} \right)}}} = {{E_{X\sim D}\left\{ {\log \;\left\lbrack {(X)} \right\rbrack} \right\}} + {E_{{G{(z)}} \sim D^{\prime}}\left\{ {\log \left\lbrack {1 - {\left( {(z)} \right)}} \right\rbrack} \right\}} + {E_{{G{(z)}}\sim D^{\prime}}\left\{ {1 - {\Delta \left\lbrack {X,{(z)}} \right\rbrack}} \right\}}}} & (14) \end{matrix}$

where K(

,

) denotes the overall cost function, D′ denotes a probability distribution of the generator-output vectors (e.g., vector 1012), D denotes the probability distribution of vectors (e.g., denoted by c₁, c₂, . . . c_(k)) representing the training data 824 (e.g., as part of input words 802), and Δ[X,

(z)] is a normalized distance metric which is 0 when X=

(z), that is, the value will be 0 when the one or more input words 802 (e.g., X) and the generator-generated character sequence represented by generator-output vectors (e.g.,

(z)) are the same word.

For example, if the one or more input words 802 is “move” and the generator-generated character sequence is also “move,” Δ[X,

(z)] is 0. As the one or more input words 802 (e.g., denoted by X) and the generator-generated character sequence represented by generator-output vectors (e.g., denoted by

(z)) become more dissimilar, the value of Δ[X,

(z)] will increase, approaching 1 when the one or more input words 802 (e.g., denoted by X) and the generator-generated character sequence represented by generator-output vectors (e.g., denoted by

(z)) are completely different. For example, if the one or more input words 802 is “move” and the generator-generated character sequence represented by generator-output vectors is “aldc,” the value of Δ[X,

(z)] is 1. In above formula (14), by maximizing the overall cost function (denoted by

(

,

)) over discriminator 860 while minimizing over generator 840, the learning network 830 determines one or more output words that are maximally dissimilar from one or more input words 802, while still the same or similar to (e.g., seeming to be drawn from) the distribution of the training data 824.

Thus, the unsupervised training of learning network 830 can be determined to be complete based on two probability distributions. The first probability distribution is associated with the training data 824 (e.g., a collection of words having typographical errors made by human users) and the second probability distribution is associated with the generator-output vectors (e.g., vectors generated by generator 840) representing the generator-generated character sequence. The unsupervised training is determined to be complete if the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is minimized (e.g., the generator-generated character sequence has a similar or same probability distribution as that of the training data 824, which is a collection of words having typographical errors known to be made by human users).

In some examples, the unsupervised training of learning network 830 is not determined to be complete if the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is not minimized. In some examples, if the unsupervised training of learning network 830 is not determined complete at least one of the parameters of the generator 840, the seed data 822, and the parameters of the discriminator 860 can be updated to produce a different result.

Once the unsupervised training of the learning network 830 is complete, learning network 830 can determine one or more output words that have the same or similar probability distributions to training data 824 (e.g., the collection of words having typographical errors known to be made by human users). For example, if one of the words in training data 824 is “moove,” which has a typographical error made by a user, one or more output words generated by learning network 830 may include “loove” (a typographical error of “love”), or “coove” (a typographical error of “cove”). The output words can be words having realistic typographic errors as if those were made by actual human users.

As described above, in some examples, while the learning network 830 is performing unsupervised training, the seed data 822 can be perturbed to produce additional generator-output vectors representing one or more additional words including different typographical errors. In some examples, the seed data 822 is perturbed by randomly adding a small value to one or more coordinates of the seed vector. The seed data 822 can be perturbed any number of times to produce any number of character sequences that is desired to train learning network 880 to produce typographical error model 882, as illustrated in FIG. 8.

In some examples, the seed data 822 is adjusted by changing the one or more input words 802 and performing the extraction of seed data 822 based on the changed one or more input words 802. For example, after generating all possible generator-output vectors based on the one or more input words 802 “move” (e.g., by perturbing the seed vector corresponding to input word “move” during the unsupervised training), the one or more input words 802 may be changed to “nuclear,” and the seed data 822 may be re-extracted based on the one or more input words 802 “nuclear.” Once the seed data 822 is re-extracted based on the one or more input words 802 “nuclear,” generator-output vectors may be generated from learning network 830 based on the re-extracted seed data 822 (e.g., by perturbing the seed vector corresponding to input word “nuclear”). Thus, in some examples, every single seed vector is perturbed as part of the unsupervised training, independent of the identity of the associated input word. After the learning network 830 generates a desired number of generator-output vectors representing one or more output words, the generator-output vectors and/or the words represented by the generator-output vectors are provided as a data set for performing subsequent supervised training of learning network 880.

Returning to FIG. 8, the data set 832 including one or more output words determined by learning network 830 and/or the corresponding vectors and the one or more input words 802 is provided to learning network 880. Learning network 880 can then be trained in a supervised manner using data set 832 to generate typographical error model 882. In some examples, typographical error model 882 can be used to determine corrected candidate words based on user inputs including typographical errors. For example, typographical error model 882 can recognize that a user meant to input “move” when they instead input the word including a typographical error “moove,” even if the error “moove” has not been made by the user before. As another example, the typographical error model 882 generated by the above described techniques can be used to recognize a word that has typographical error in the beginning of the word. Existing spell-check engine typically cannot recognize an error if the error is in the beginning of the word. In some examples, typographical error model 882 can be used with or integrated into the digital assistant at a user device and/or a digital assistant at a server. In some examples, typographical error model 882 can be used for or integrated into different applications of a user device.

By training the learning network 830 (e.g., the GAN) in an unsupervised manner a large number of words including realistic typographical errors that include both atomic and non-atomic typographical errors is generated. In this way, a body of training data that includes typographical errors that resemble typographical errors that a human user would make can be generated in an efficient and practical manner for integration into a typographical error model of a digital assistant. Thus, the improved typographical error model including more realistic typographical errors can be practically generated, representing an improvement over conventional typographical error models. Further, the improved typographical error model may provide more accurate detection of typographical errors and more intelligent corrections to the user, resulting in improved user interaction.

5. Exemplary Functions and Architectures of a Digital Assistant Detecting and Correcting Typographical Errors.

FIG. 11 illustrates a user interface for receiving user input text, according to various examples. As illustrated in FIG. 11, an electronic device 1100 provides a user interface 1102 for receiving and displaying user input including one or more words. For example, a user may enter text (e.g., by using a keyboard or voice input) into user interface 1102 for incorporation into an email, text message, or other communication. In some examples, the user may enter text into user interface 1102 for incorporation into another application to perform a task corresponding to the user input. In one example, the user provides input 1104 including one or more words on user interface 1102, such as the word “moove.” The electronic device 1100 then displays the user input 1104 “moove” on the user interface 1102. User input 1104 can then be provided to a digital assistant for typographical error detection.

FIG. 12 illustrates a block diagram of a digital assistant 1200 for providing a corrected word 1280, according to various examples. In some examples, digital assistant 1200 (e.g., digital assistant system 700) is implemented by a user device (e.g., user device 1100) according to various examples. In some examples, the user device, a server (e.g., server 108), or a combination thereof, can implement digital assistant 1200. The user device can be implemented using, for example, device 104, 200, 400, 600, 1100, or 1300 as illustrated in FIGS. 1, 2A-2B, 4, 6A-6B, 11, and 13A-13B. In some examples, digital assistant 1200 can be implemented using digital assistant module 726 of digital assistant system 700. Digital assistant 1200 includes one or more modules, models, applications, vocabularies, and user data similar to those of digital assistant module 726. For example, digital assistant 1200 includes the following sub-modules, or a subset or superset thereof: an input/output processing module, an STT process module, a natural language processing module, a task flow processing module, and a speech synthesis module. These modules can also be implemented similar to that of the corresponding modules as illustrated in FIG. 7B, and therefore are not shown and not repeatedly described.

In some embodiments, digital assistant 1200 includes a trained first learning network 1240 and a classifier 1260. In some examples, the trained first learning network 1240 can be an instance of trained learning network 880. For example, as described above, learning network 880 can be trained in a supervised manner to provide a typographical error model 882. In some examples, the training of learning network 880 can be performed at a digital assistant operating on a server. An instance of the trained learning network 880 can then be installed or provisioned to a user device (e.g., device 1300) as trained learning network 1240. A typographical error model 882 can be included in or user with a classifier 1260 for correcting typographical errors associated with user input 1220. In some example, trained learning network 1240 can be re-trained or updated as more user inputs are available. Based on the retraining or updating of the trained learning network 1240, the typographical error model 882 can also be updated. In some example, trained learning network 1240 operates on a server and is an instance of trained learning network 880. As described above, learning network 880 is trained in a supervised manner based on data set 832 generated by learning network 830. Further, as described above, learning network 830 is trained in an unsupervised manner based on the one or more input words 802 and the training data 824 that includes a collection of words with typographical errors (e.g., a limited collection of words with errors made by human users).

With reference to FIG. 11 and FIG. 12, the user input 1104 can be provided to a digital assistant 1200 as user input 1220. As illustrated by FIG. 12, the user input 1220 is then provided to classifier 1260, which uses a typographical error model 882 for typographical error detection.

Typographical error model 882 enables the determination of whether the user input 1220 includes a typographical error. In some examples, to determine whether the user input 1220 includes a typographical error, classifier 1260 determines, using the typographical error model 882 (or an updated model, collectively as typographical error model 882) one or more incorrect characters of the user input 1220. In some examples, to determine whether the user input 1220 includes a typographical error, classifier 1260 determines, using typographical error model 882 (or an update model) whether the typographical error is a non-atomic or atomic typographical error. As discussed above, a word having non-atomic typographical error is lexically incorrect, and a word having an atomic typographical error is lexically correct but contextually incorrect.

In some examples, the classifier 1260, using typographical error model 882, determines if the typographical error is a non-atomic or atomic typographical error based on the user input 1220. In some examples, the classifier 1260, using typographical error model 882, determines if the typographical error is a non-atomic or atomic typographical error based a context of the user input 1220. In some examples, the context of the user input includes one or more words preceding a current word of the user input 1220. In some examples, the context of the user input includes one or more words following a current word of the user input 1220.

For example, a user input 1220 can include “please help me moove this weekend.” In this example, because the word “moove” is lexically incorrect, the classifier, using typographical error model 882, can determine that “moove” is a non-atomic typographical error using pairs of a valid word “move” and a plurality of words having typographic errors associated with the valid word (e.g., pairs of {w, Y_(w)}, where Y_(w) is a set of generator-generated words having typographical errors). In some examples, a user input 1220 can include “your answer is nuclear.” In this example, the classifier 1260, using typographical error model 882, can determine that “nuclear” is lexically correct and thus is not a non-atomic typographical error. Moreover, based on contextual data, the classifier 1260, using typographical error model 882, can determine that pairs of a valid word “unclear” and a plurality of words having typographic errors associated with the valid word (e.g., pairs of {w, Y_(w)}) include a pair {unclear, nuclear}. That is, the word “nuclear” may be an atomic error for the word “unclear.” Thus, based on the typographical error model 882 and contextual data associated with the user input 1220 (e.g., the words “your answer is” as context of“nuclear”), the classifier 1260 determines that while “nuclear” is lexically correct, based on the context it is not the word the user intended. Thus, the classifier 1260 can determine that “nuclear” is an atomic typographical error within the user input 1220 “your answer is nuclear.”

As previously discussed, the classifier 1260, using typographical error model 882, may also consider one or more words following a current word of the user input 1220 as context. For example, a user input 1220 can include “the prostate figure on the ground.” In this example, the classifier 1260, using typographical error model 882, can determine that “prostate” is lexically correct and thus is not a non-atomic typographical error. Moreover, the classifier 1260, using typographical error model 882, can determine that pairs of a valid word “prostrate” and a plurality of words having typographic errors associated with the valid word (e.g., pairs of {w, Y_(w)}) include a pair {prostrate, prostate}. That is, the word “prostate” may be an atomic error for the word “prostrate.” Thus, based on the typographical error model 882 and contextual data associated with the user input 1220 (e.g., the words “the . . . figure on the ground” as context of “prostate”), classifier 1260 can determine that while “prostate” is lexically correct, based on the context it is not the word the user intended. Thus, classifier 1260 can determine that “prostate” is an atomic typographical error within the user input 1220 “the prostate figure on the ground.”

As illustrated in FIG. 12, if the classifier 1260, using typographical error model 882, detects a typographical error in the user input 1220, classifier 1260 can provide one or more candidate words 1280 based on the user input 1220 and the typographical error(s) detected. For example, classifier 1260 can determine that the user input 1220 “moove” includes a typographical error. Classifier 1260 may further determine, using the pairs of valid word-typographical error words included in typographical error model 882, that the typographical error is an extra inserted letter or a swap of a letter. Based on the determination, classifier 1260 can then determine one or more candidate words 1280 absent typographical errors based on the provided information. For example, classifier 1260 can determine, using one or more pairs of valid word-typographical error words included in typographical error model 882, that candidate words 1280 absent typographical errors include “move,” “moose,” or “movie.”

In some examples, classifier 1260 can determine candidate words 1280 absent typographical errors based on a context of the user input 1220. In some examples, the context of the user input 1220 can include one or more words preceding or following a current word (e.g., the word being inspected for potential typographical errors) of the user input 1220. For example, if a user input 1220 is “please help me moove this weekend” the classifier 1260 can use the context of “please help me . . . this weekend” to determine candidate words 1280 absent typographical errors include “move.”

In some examples, if classifier 1260 does not detect a typographical error in the user input 1220, it forgoes providing one or more candidate words 1280. In some examples, if classifier 1260 does not provide any candidate words 1280, digital assistant 1200 continues to display the user input 1104 in the user interface 1102 without providing candidate words 1280.

FIGS. 13A and 13B illustrate user interfaces for providing candidate words 1280 absent typographical errors to the user, according to various examples. With reference to FIG. 13A, candidate words 1280A-C absent typographical errors are displayed by electronic device 1300 on user interface 1302. The user can then provide a user selection using one or more fingers 302 to select one of candidate words 1280A-C. After the selection of a candidate word is received from the user, the selected candidate word will replace the original user input 1304 in the user interface so that a corrected user input is displayed.

With reference to FIG. 12 and FIG. 13A, in one example, the user provides the user input 1304 “moove,” to user interface 1302. Digital assistant 1200 operating on electronic device 1300 provides the user input 1304 to classifier 1260 which detects that “moove” has a typographical error using typographical error model 882. Based on the detected typographical error, classifier 1260 can determine, the candidate words such as “move,” “moose,” and “movie.” Digital assistant 1200 can display “move,” “moose,” and “movie,” as candidate words 1280A-C on user interface 1302, respectively. Thus, the candidate words “move,” “moose,” and “movie” can be presented to the user for selection in user interface 1302 as candidate words 1280A-C, respectively. The user may then select one of the displayed candidate words 1280A-C to replace the user input 1304 “moove.” For example, the user can provide a user selection 302 to select “move” (e.g., candidate word 1280A) as the correct candidate word and the displayed user input 1304 “moove” can be replaced with “move” (e.g., candidate word 1280A), so that the displayed user input is corrected.

In some examples, correcting the displayed user input 1304 includes deleting the displayed user input 1304 and displaying a selected candidate word (e.g., word 1280A). In some examples, correcting the displayed user input 1304 includes deleting an incorrect character of displayed user input 1304 so that displayed user input 1304 matches a selected candidate word (e.g., deleting the extra character “o” from displayed user input 1304). In some examples, correcting the displayed user input 1304 includes adding a character to displayed user input 1304 so that displayed user input 1304 matches a selected candidate word. In some examples, correcting the displayed user input 1304 includes deleting one or more incorrect characters of displayed user input 1304 and adding one or more characters to displayed user input 1304 so that displayed user input 1304 matches a selected candidate word 1306.

With reference to FIG. 12 and FIG. 13B, in some examples, the user provides the user input 1304 “moove,” to user interface 1302. The user interface 1302 displays the user input 1304 (e.g., “moove”) when the user inputs the text. Digital assistant 1200 operating on device 1300 provides the user input 1304 classifier 1260, which detects that user input 1304 includes a typographical error. Classifier 1260 of digital assistant 1200, using typographical error model 882 determines, based on the detected typographical error, a plurality of candidate words absent typographical errors. In some examples, the classifier 1260 can then determine a ranking of the plurality of candidate words absent typographical errors and provide the highest ranked candidate word 1312 to be displayed on the user interface 1302. The digital assistant 1200 operating on device 1300 can then correct the displayed user input 1304 with the highest ranked candidate word 1312 (e.g., “move”).

In some examples, the ranking of the plurality of candidate words is based on which candidate word is most frequently selected by users (e.g., the candidate words popularity). In some examples, the ranking of the plurality of candidate words is based on the context associated with the user input 1304. In some examples, the context associated with the user input 1304 includes words or characters preceding or following the current word or character of the user input 1304. In some examples, the context associated with the user input 1304 includes the sentence structure of the user input 1304 and whether a portion of the user input 1304 is at the beginning or end of a sentence.

For example, as illustrated in FIG. 13B, a user provides a user input 1304 of“moove.” The classifier 1260, using typographical error model 882 determines that the user input 1304 “moove” includes a typographical error and then determines a plurality of candidate words absent typographical errors, such as “move,” “moose,” and “movie.” The classifier 1260 can then determine a ranking of the “move,” “moose,” and “movie,” based on the popularity of each of each of the candidate words. For example, the classifier 1260 may determine that the particular user or a group of users most often select the word “move,” followed by “movie,” and then “moose.” Thus, the classifier 1260 may determine that “move” is the highest ranked candidate word absent typographical errors and provide “move” as candidate word 1312. In some examples, the user input 1304 may be “please help me moove this weekend.” In this example, the classifier 1260 may evaluate the user input “moove” by considering the words “please help me,” and “this weekend” as context, to determine that the highest ranked candidate word is “move.”

In some examples, a user input can include an atomic typographical error. For example, with reference to FIG. 12 a user input 1220 may be “your answer is nuclear.” In this example, the classifier 1260 can determine that possible candidate words include “unclear” and “nucleus.” The classifier 1260 can then consider the words “your answer is” as preceding context of “nuclear” to determine that the highest ranked candidate word is “unclear,” and provide “unclear” as a candidate word. Additionally, the classifier 1260 can determine that “unclear” is the highest ranked candidate word based on, for example, “unclear” being the most often selected candidate word by users.

In some examples, as shown in FIG. 13B, the digital assistant operating on device 1300 automatically corrects the displayed user input 1304 with the highest ranked candidate word 1312. In some examples, correcting the displayed user input 1304 includes deleting at least a portion of the displayed user input 1304 and displaying the highest ranked candidate word 1312 (e.g., replacing “moove” with “move”). In some examples, correcting the displayed user input 1304 includes deleting an incorrect character of displayed user input 1304 so that displayed user input 1304 matches the highest ranked candidate word 1312 (e.g., deleting the extra letter “o” from “moove”). In some examples, correcting the displayed user input 1304 includes adding a character to the displayed user input so that the displayed user input matches the highest ranked candidate word (e.g., adding a letter “r” to “prostate”, not shown in FIG. 13B). In some examples, correcting the displayed user input includes deleting one or more incorrect characters of the displayed user input and adding one or more characters to the displayed user input so that the displayed user input matches the highest ranked word.

As previously discussed, by incorporating the typographical error model 882 into digital assistant 1200, improved and more intelligent detection and correction of typographical errors can be provided without having to collect all conceivable typographical errors. As a result, the digital assistant 1200 can provide more accurate detection and correction of both atomic and non-atomic typographical errors over conventional typographical error detection systems. Further, in some examples, typographical error model 882 is similar in size to conventional typographical error language models and thus provides an improvement in performance without or with little negative effects related to storage or battery life of a personal electronic device.

Additionally, as previously discussed, the improved typographical error model is application independent and thus can provide intelligent typographical error detection and correction anytime the digital assistant is operative. In particular, a digital assistant can monitor user input continuously, independent of which application is active to provide a user with typographical error detection and correction when performing a variety of tasks.

FIGS. 14A-14D illustrate an exemplary process 1400 for operating a digital assistant to provide typographical error detection and correction, according to various examples. Process 1400 is performed, for example, using one or more electronic devices implementing a digital assistant. In some examples, process 1400 is performed using a client-server system (e.g., system 100) and the blocks of process 1400 are divided up in any manner between the server (e.g., DA server 106) and a client device. In other examples, the blocks of process 1400 are divided up between the server and multiple client device (e.g., a mobile phone and a smart watch). Thus, while portion of process 1400 are described herein as being performed by particular device of a client-server system, it will be appreciated that process 1400 is not so limited. In other examples, process 1400 is performed using only a client device (e.g., user device 104) or only multiple client devices. In process 1400, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 1400.

With reference to FIG. 14A, at block 1410, one or more input words (e.g., input words 802 as shown in FIG. 8) are received. As described above, a typographical error includes at least one of a non-atomic typographical error or an atomic typographical error.

At block 1420, seed data (e.g., seed data 822 as shown in FIG. 8) for unsupervised training of a first learning network (e.g., learning network 830 as shown in FIG. 8) is extracted. The seed data for unsupervised training of a first learning network is based on the one or more input words (e.g., input words 802 as shown in FIG. 8).

At block 1421, an input character sequence corresponding to each of the one or more input words (e.g., input words 802 as shown in FIG. 8) is obtained. At block 1422, the input character sequence is encoded. In some examples, the input sequence is encoded by representing each character of the input character sequence using 1-of-N encoding at block 1423.

At block 1424, a vector representing at least a portion of the seed data is determined. In some examples, the vector representing at least a portion of the seed data is encoded with contextual data associated with the one or more input words (e.g., input words 802 as shown in FIG. 8). In some examples, the contextual data associated with the one or more input words includes words preceding a current word of the one or more input words and following the current word of the one or more input words.

At block 1425, a plurality of first interim vectors (e.g., 922 and 924 as shown in FIG. 9) encoded with the contextual data associated with the one or more input words (e.g., input words 802 as shown in FIG. 8) is generated. In some examples, the plurality of first interim vector are generated using a first recurrent neural network layer (e.g., 920), as shown in FIG. 9.

At block 1426, an interim vector (e.g., 922 as shown in FIG. 9) representing preceding context of the current character is determined for a current character (e.g., 912 as shown in FIG. 9) of the encoded input character sequence. At block 1427 an interim vector (e.g., 924 as shown in FIG. 9) representing following context of the current character is determined for a current character (e.g., 912 as shown in FIG. 9) of the encoded input character sequence.

At block 1428, the plurality of first interim vectors are aggregated. At block 1429, a plurality of second interim vectors (e.g., 932 and 934 as shown in FIG. 9) are obtained based on the results of aggregating the plurality of first interim vectors. In some examples, the total number of second interim vectors is less than the total number of the first interim vectors.

At block 1430, the vector representing at least a portion of the seed data is generated based on the plurality of second interim vectors (e.g., 932 and 934 as shown in FIG. 9). At block 1431 a plurality of third interim vectors (e.g., 942 and 944 as shown in FIG. 9) are generated based on the plurality of second interim vectors using a second recurrent neural network layer (e.g., 940 as shown in FIG. 9).

With reference to FIG. 14B, at block 1432, the plurality of third interim vectors are aggregated. At block 1434 the vector representing at least a portion of the seed data (e.g., seed data 822 as shown in FIG. 8) is generated based on the results of aggregating the plurality of third interim vectors.

At block 1440, training data (e.g., training data 824 as shown in FIG. 8) for the first learning network (e.g., learning network 830 as shown in FIG. 8) is obtained. In some examples, the training data includes a collection of words having typographical errors. In some examples, the training data for the first learning network comprises a plurality of words collected from a plurality of users, each word of the plurality of words including a typographical error made by the one of the plurality of the users.

At block 1460, one or more output words having a probability distribution corresponding to a probability distribution of the training data (e.g., training data 824 as shown in FIG. 8) are determined. In some examples, the one or more output words are determined using the first learning network (e.g., learning network 830 as shown in FIG. 8) and based on the seed data (e.g., seed data 822 as shown in FIG. 8) and the training data.

At block 1461, an unsupervised training of the first learning network (e.g., learning network 830 as shown in FIG. 8) based on the seed data (e.g., seed data 822 as shown in FIG. 8) and the training data (e.g., training data 824 as shown in FIG. 8) is performed. In some examples, the first learning network is a generative adversarial network comprising a generator (e.g., generator 840 as shown in FIG. 8) and a discriminator (e.g., discriminator 860 as shown in FIG. 8).

At block 1462, a first probability distribution associated with the training data is determined. At block 1463, representations of a generated character sequence representing a word having one or more typographical errors is generated. At block 1464, a second probability distribution associated with the representations of a generated character sequence is determined.

With reference to FIG. 14C, at block 1465, it is determined whether the unsupervised training is completed based on the first and second probability distributions. In some examples, blocks 1463 through 1465 occur in each step of the unsupervised training, until it is determined that the unsupervised training is completed.

At block 1466, it is determined whether the difference between an expectation with respect to the first probability distribution and the expectation with respect to the second probability distribution is minimized.

At block 1467, a discriminator-output vector (e.g., q′ 1022 as shown in FIG. 10) indicating the probability of the one or more output words having a probability distribution corresponding to the probability distribution of the training data is determined. In some examples, the discriminator-output vector is determined by the discriminator (e.g., discriminator 860 as shown in FIG. 8) and is based on the representations of a generated character sequence. At block 1468, the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is determined based on the discriminator-output vector.

At block 1469, in accordance with a determination that the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is minimized, it is determined that the unsupervised training of the first learning network is completed.

With reference to FIG. 14D, at block 1470, in accordance with a determination that the difference between an expectation with respect to the first probability distribution and an expectation with respect to the second probability distribution is not minimized, then at least one of updating at least one of the parameters of the generator and the seed data and updating the parameters of the discriminator is performed. At block 1471, the one or more output words having a probability distribution corresponding to the probability distribution of the training data are determined. In some examples, the one or more output words are determined based on the training results of the first learning network.

At block 1480, a data set of supervised training of a second learning network is generated. In some examples, the data set is based on the determined one or more output words. In some examples, the trained second learning provides one or more typographical error correction suggestions. In some examples, the trained second learning network provides one or more typographical error correction suggestions via a user device.

In some examples, the process further includes perturbing the seed data and determining using the first learning network and based on the perturbed seed data and the training data, a second set of output words having a probability distribution corresponding to the probability distribution of the training data. In some examples, perturbing the seed data includes adding a small value to one or more coordinates of the seed vector.

FIG. 15 illustrates process 1500 for operating a digital assistant to provide typographical error detection and correction, according to various examples. At block 1510 a user input (e.g., user input 1104) including one or more words is received. At block 1520 the user input is displayed. In some examples, the user input containing a typographic error may not be displayed when automatic correction of the typographic error is performed, as described below. At block 1540 it is determined whether the user input includes a typographical error using a first trained learning network (e.g., learning network 880 as shown in FIG. 8).

In some examples, the first learning network (e.g., learning network 880 as shown in FIG. 8) is trained in a supervised manner based on a data set generated by a second learning network. (e.g., learning network 830 as shown in FIG. 8). In some examples, the second learning network is trained in an unsupervised manner based on input words (e.g., input words 802 as shown in FIG. 8) and training data (e.g. training data 824 as shown in FIG. 8) that includes a collection of words having typographical errors.

In some examples, the data set (e.g., data set 832 as shown in FIG. 8) is generated by a second learning network (e.g., learning network 830 as shown in FIG. 8) on a second electronic device. In some examples, the second electronic device includes one or more programs for: receiving one or more input words (e.g., input words 802 as shown in FIG. 8); extracting, based on the one or more input words, seed data (e.g., seed data 822 as shown in FIG. 8) for unsupervised training of the second learning network; obtaining the training data that includes a collection of words having typographical errors; determining using the second learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data; and generating based on the determined one or more output words, the data set of supervised training of the first learning network (e.g., learning network 880 as shown in FIG. 8). In some examples, the one or more output words include typographical errors.

In some examples, extracting, based on the one or more input words (e.g., input words 802 as shown in FIG. 8), the seed data for unsupervised training of the second learning network (e.g., learning network 830 as shown in FIG. 8) includes obtaining an input character sequence corresponding to each of the one or more input words, encoding the input character sequence, and determining, based on the encoded input character sequence, a vector representing at least a portion of the seed data, wherein the vector is encoded with contextual data associated with the one or more input words.

In some examples, determining the one or more output words having a probability distribution corresponding to the probability distribution of the training data includes performing an unsupervised training of the second learning network (e.g., learning network 830 as shown in FIG. 8) based on the seed data (e.g., seed data 822 as shown in FIG. 8) and the training data (e.g., training data 824 as shown in FIG. 8), and determining based on the training results of the second learning network, the one or more output words having a probability distribution corresponding to the probability distribution of the training data. In some examples, the second learning network is a generative adversarial network comprising a generator (e.g., generator 840 as shown in FIG. 8) and a discriminator (e.g., discriminator 860 as shown in FIG. 8).

In some examples, performing the unsupervised training of the second learning network (e.g., learning network 840 as shown in FIG. 8) based on the seed data (e.g., seed data 822 as shown in FIG. 8) and the training data (e.g., training data 824 as shown in FIG. 8) includes determining a first probability distribution with the training data, and in each iteration of the unsupervised training, generating, by the generator (e.g., generator 840 as shown in FIG. 8), representations of a generated character sequence representing a word having one or more typographical errors, determining a second probability distribution associated with the representations of a generated character sequence, and determining whether the unsupervised training is completed based on the first and second probability distributions.

In some examples, the training data that includes a collection of words having typographical errors includes a plurality of words collected from a plurality of users, each word of the plurality of words including a typographical error made by one of the plurality of users.

In some examples, a typographical error includes at least one of a non-atomic typographical error or an atomic typographical error. In some examples, a word having a non-atomic typographical error is lexically incorrect. In some examples, a word having an atomic typographical error is lexically correct but contextually incorrect.

At block 1542, it is determined based on at least one of the user input or a context of the user input, whether the typographical error is a non-atomic typographical error or an atomic typographical error. In some examples, the context of the user input includes at least one of words preceding a current word of the user input or words following the current word of the user input.

At block 1560, in accordance with a determination that the user input includes a typographical error, the displayed user input (e.g., user input 1104 as shown in FIG. 11) is corrected. In some examples, correcting the displayed user input includes determining, using a typographical error model associated with the trained first learning network, a plurality of candidate words (e.g., candidate words 1280A-B as shown in FIG. 13A) absent typographical errors; providing the plurality of candidate words absent typographical errors to the user; receiving, from the user, a selection (e.g., user selection 302 as shown in FIG. 13A) of one candidate word of the plurality of candidate words; and in response to receiving the user selection, displaying a corrected user input.

In some examples, displaying the corrected user input includes deleting the displayed user input (e.g., user input 1304 as shown in FIG. 13A) and displaying the selected candidate word (e.g., candidate word 1280A as shown in FIG. 13A). In some examples, determining whether the user input includes a typographical error includes determining, using a typographical error model (e.g., typographical error model 822 as shown in FIG. 12) associated with the trained first learning network one or more incorrect characters of a word of the user input. In some examples, displaying the corrected user input includes deleting the one or more incorrect characters of the word of the user input and inserting one or more correct character into the word of the user input.

In some examples, correcting the displayed user input includes determining, using a typographical error model (e.g., typographical error model 822 as shown in FIG. 12) associated with the trained first learning network (e.g. learning network 1240 as shown in FIG. 12), a plurality of candidate words absent typographical errors, determining a ranking of the plurality of candidate words absent typographical errors, and correcting the displayed user input (e.g., user input 1304 as shown in FIG. 13B) with the highest ranked candidate word (e.g., candidate word 1312 as shown in FIG. 13B). In some examples, the ranking of the plurality of candidate words is based on the popularity of the candidate words. In some examples, the ranking of the plurality of candidate words is based on the context associated with the user input.

In some examples, correcting the displayed user input (e.g., user input 1304 as shown in FIG. 13B) with the highest ranked candidate word (e.g., candidate word 1312 as shown in FIG. 13B) includes deleting at least a portion of the displayed user input and displaying the highest ranked candidate word. In some examples, determining whether the user input includes a typographical error includes determining, using a typographical error model (e.g., typographical error model 822 as shown in FIG. 12) associated with the trained first learning network, one or more incorrect characters of a word of the user input. In some examples, correcting the displayed user input with the highest ranked candidate word includes deleting one or more incorrect characters of the word of the user input and inserting one or more characters into the word of the user input.

In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.

Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.

As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve the delivery of typographical error corrections or any other content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter IDs, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver more accurate typographical error corrections to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure.

The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.

Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide mood-associated data for targeted content delivery services. In yet another example, users can select to limit the length of time mood-associated data is maintained or entirely prohibit the development of a baseline mood profile. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.

Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.

Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publicly available information. 

What is claimed is:
 1. A non-transitory computer-readable storage medium storing one or more programs for detecting a typographical error in a user input, the one or more programs comprising instruction, which when executed by one or more processors of an electronic device, cause the electronic device to: receive the user input including one or more words; display the user input; determine, using a trained first learning network, whether the user input includes a typographical error, wherein the first learning network is trained in a supervised manner based on a data set generated by a second learning network, and wherein the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors; and in accordance with a determination that the user input includes a typographical error, correct the displayed user input.
 2. The non-transitory computer-readable storage medium of claim 1, wherein the data set generated by the second learning network is generated using one or more programs comprising instruction, which when executed by one or more processors of a second electronic device, cause the second electronic device to: receive one or more input words; extract, based on the one or more input words, seed data for unsupervised training of the second learning network; obtain the training data that includes a collection of words having typographical errors; determine, using the second learning network and based on the seed data and the training data, one or more output words having a probability distribution corresponding to a probability distribution of the training data, wherein the one or more output words include typographical errors; and generate, based on the determined one or more output words, the data set for supervised training of the first learning network.
 3. The non-transitory computer-readable storage medium of claim 2, wherein extracting, based on the one or more input words, the seed data for unsupervised training of the second learning network comprises: obtaining an input character sequence corresponding to each of the one or more input words; encoding the input character sequence; and determining, based on the encoded input character sequence, a vector representing at least a portion of the seed data, wherein the vector is encoded with contextual data associated with the one or more input words.
 4. The non-transitory computer-readable storage medium of claim 2, wherein determining the one or more output words having a probability distribution corresponding to the probability distribution of the training data comprises: performing an unsupervised training of the second learning network based on the seed data and the training data, wherein the second learning network is a generative adversarial network comprising a generator and a discriminator; and determining based on the training results of the second learning network, the one or more output words having a probability distribution corresponding to the probability distribution of the training data.
 5. The non-transitory computer-readable storage medium of claim 4, wherein performing the unsupervised training of the second learning network based on the seed data and the training data comprises: determining a first probability distribution associated with the training data; and in each iteration of the unsupervised training, generating, by the generator, representations of a generated character sequence representing a word having one or more typographical errors; determining a second probability distribution associated with the representations of a generated character sequence; and determining whether the unsupervised training is completed based on the first and second probability distributions.
 6. The non-transitory computer-readable storage medium of claim 1, wherein the training data that includes a collection of words having typographical errors comprises a plurality of words collected from a plurality of users, each word of the plurality of words including a typographical error made by one of the plurality of users.
 7. The non-transitory computer-readable storage medium of claim 1, wherein a typographical error includes at least one of a non-atomic typographical error or an atomic typographical error, wherein a word having a non-atomic typographical error is lexically incorrect and a word having an atomic typographical error is lexically correct but contextually incorrect.
 8. The non-transitory computer-readable storage medium of claim 7, wherein determining whether the user input includes a typographical error comprises: determining, based on at least one of the user input or a context of the user input, whether the typographical error is a non-atomic typographical error or an atomic typographical error.
 9. The non-transitory computer-readable storage medium of claim 8, wherein the context of the user input comprises at least one of words preceding a current word of the user input or words following the current word of the user input.
 10. The non-transitory computer-readable storage medium of claim 1, wherein correcting the displayed user input comprises: determining, using a typographical error model associated with the trained first learning network, a plurality of candidate words absent typographical errors; providing the plurality of candidate words absent typographical errors to the user; receiving, from the user, a selection of one candidate word of the plurality of candidate words; and in response to receiving the user selection, displaying a corrected user input.
 11. The non-transitory computer-readable storage medium of claim 10, wherein displaying the corrected user input comprises: deleting the displayed user input; and displaying the selected candidate word.
 12. The non-transitory computer-readable storage medium of claim 10, wherein determining whether the user input includes a typographical error comprises determining, using the typographical error model associated with the trained first learning network, one or more incorrect characters of a word of the user input.
 13. The non-transitory computer-readable storage medium of claim 12, wherein displaying the corrected user input comprises: deleting the one or more incorrect characters of the word of the user input; and inserting one or more correct characters into the word of the user input.
 14. The non-transitory computer-readable storage medium of claim 1, wherein correcting the displayed user input comprises: determining, using a typographical error model associated with the trained first learning network, a plurality of candidate words absent typographical errors; determining a ranking of the plurality of candidate words absent typographical errors; and correcting the displayed user input with the highest ranked candidate word.
 15. The non-transitory computer-readable storage medium of claim 14, wherein the ranking of the plurality of candidate words is based on the popularity of the candidate words.
 16. The non-transitory computer-readable storage medium of claim 14, wherein the ranking of the plurality of candidate words is based on the context associated with the user input.
 17. The non-transitory computer-readable storage medium of claim 14, wherein correcting the displayed user input with the highest ranked candidate word comprises: deleting at least portion of the displayed user input; and displaying the highest ranked candidate word.
 18. The non-transitory computer-readable storage medium of claim 14, wherein determining whether the user input includes a typographical error comprises determining, using a typographical error model associated with the trained first learning network, one or more incorrect characters of a word of the user input.
 19. The non-transitory computer-readable storage medium of claim 18, wherein correcting the displayed user input with the highest ranked candidate word comprises: deleting the one or more incorrect characters of the word of the user input; and inserting one or more correct characters into the word of the user input.
 20. A method for detecting a typographical error in a user input, comprising: at an electronic device with one or more processors and memory: receiving the user input including one or more words; displaying the user input; determining, using a trained first learning network, whether the user input includes a typographical error, wherein the first learning network is trained in a supervised manner based on a data set generated by a second learning network, and wherein the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors; and in accordance with a determination that the user input includes a typographical error, correcting the displayed user input.
 21. An electronic device, comprising: one or more processors; memory; and one or more programs stored in memory, the one or more programs including instructions for: receiving a user input including one or more words; displaying the user input; determining, using a trained first learning network, whether the user input includes a typographical error, wherein the first learning network is trained in a supervised manner based on a data set generated by a second learning network, and wherein the second learning network is trained in an unsupervised manner based on input words and training data that includes a collection of words having typographical errors; and in accordance with a determination that the user input includes a typographical error, correcting the displayed user input. 